machinelearning
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machinelearning
890 files • 517.03 MB
Volume 01
19 files • 21.71 MB
#1
5 files • 7.45 MB
00 - Editorial. On Machine Learning.pdf
machinelearning/Volume 01/#1/00 - Editorial. On Machine Learning.pdf
01 - Chunking in Soar. The Anatomy of a General Learning Mechanism.pdf
machinelearning/Volume 01/#1/01 - Chunking in Soar. The Anatomy of a General Learning Mechanism.pdf
02 - Explanation-Based Generalization. A Unifying View.pdf
machinelearning/Volume 01/#1/02 - Explanation-Based Generalization. A Unifying View.pdf
03 - Induction of Decision Trees.pdf
machinelearning/Volume 01/#1/03 - Induction of Decision Trees.pdf
04 - A Theory of Historical Discovery. The Construction of Componential Models.pdf
machinelearning/Volume 01/#1/04 - A Theory of Historical Discovery. The Construction of Componential Models.pdf
#2
4 files • 5.34 MB
00 - Editorial. The Terminology of Machine Learning.pdf
machinelearning/Volume 01/#2/00 - Editorial. The Terminology of Machine Learning.pdf
01 - Explanation-Based Learning. An Alternative View.pdf
machinelearning/Volume 01/#2/01 - Explanation-Based Learning. An Alternative View.pdf
02 - A General Framework for Induction and a Study of Selective Induction.pdf
machinelearning/Volume 01/#2/02 - A General Framework for Induction and a Study of Selective Induction.pdf
03 - News and Notes.pdf
machinelearning/Volume 01/#2/03 - News and Notes.pdf
#3
5 files • 4.82 MB
00 - Editorial. Human and Machine Learning.pdf
machinelearning/Volume 01/#3/00 - Editorial. Human and Machine Learning.pdf
01 - Experimental Goal Regression. A Method for Learning Problem-Solving Heuristics.pdf
machinelearning/Volume 01/#3/01 - Experimental Goal Regression. A Method for Learning Problem-Solving Heuristics.pdf
02 - Learning at the Knowledge Level.pdf
machinelearning/Volume 01/#3/02 - Learning at the Knowledge Level.pdf
03 - Incremental Learning from Noisy Data.pdf
machinelearning/Volume 01/#3/03 - Incremental Learning from Noisy Data.pdf
04 - News and Notes.pdf
machinelearning/Volume 01/#3/04 - News and Notes.pdf
#4
5 files • 4.1 MB
00 - Editorial. Machine Learning and Discovery.pdf
machinelearning/Volume 01/#4/00 - Editorial. Machine Learning and Discovery.pdf
01 - Integrating Quantitative and Qualitative Discovery. The ABACUS System.pdf
machinelearning/Volume 01/#4/01 - Integrating Quantitative and Qualitative Discovery. The ABACUS System.pdf
02 - Determining Arguments of Invariant Functional Descriptions.pdf
machinelearning/Volume 01/#4/02 - Determining Arguments of Invariant Functional Descriptions.pdf
03 - Chemical Discovery as Belief Revision.pdf
machinelearning/Volume 01/#4/03 - Chemical Discovery as Belief Revision.pdf
04 - News and Notes.pdf
machinelearning/Volume 01/#4/04 - News and Notes.pdf
Volume 02
22 files • 16.35 MB
#1
4 files • 4.11 MB
00 - Machine Learning and Grammar Induction.pdf
machinelearning/Volume 02/#1/00 - Machine Learning and Grammar Induction.pdf
01- Learning Syntax by Automata Induction.pdf
machinelearning/Volume 02/#1/01- Learning Syntax by Automata Induction.pdf
02 - A Version Space Approach to Learning Context-free Grammars.pdf
machinelearning/Volume 02/#1/02 - A Version Space Approach to Learning Context-free Grammars.pdf
03 - News and Notes.pdf
machinelearning/Volume 02/#1/03 - News and Notes.pdf
#2
5 files • 3.78 MB
00 - Machine learning and concept formation.pdf
machinelearning/Volume 02/#2/00 - Machine learning and concept formation.pdf
01 - Experiments with incremental concept formation. UNIMEM.pdf
machinelearning/Volume 02/#2/01 - Experiments with incremental concept formation. UNIMEM.pdf
02 - Knowledge acquisition via incremental conceptual clustering.pdf
machinelearning/Volume 02/#2/02 - Knowledge acquisition via incremental conceptual clustering.pdf
03 - A review of the fourth International Workshop on Machine Learning.pdf
machinelearning/Volume 02/#2/03 - A review of the fourth International Workshop on Machine Learning.pdf
04 - News and notes.pdf
machinelearning/Volume 02/#2/04 - News and notes.pdf
#3
6 files • 3.64 MB
00 - Research papers in machine learning.pdf
machinelearning/Volume 02/#3/00 - Research papers in machine learning.pdf
01 - Classifier systems and the animat problem.pdf
machinelearning/Volume 02/#3/01 - Classifier systems and the animat problem.pdf
02 - Learning decision lists.pdf
machinelearning/Volume 02/#3/02 - Learning decision lists.pdf
03 - Theory change via view application in instructionless learning.pdf
machinelearning/Volume 02/#3/03 - Theory change via view application in instructionless learning.pdf
04 - News and notes.pdf
machinelearning/Volume 02/#3/04 - News and notes.pdf
05 - Announcement.pdf
machinelearning/Volume 02/#3/05 - Announcement.pdf
#4
7 files • 4.82 MB
00 - New theoretical directions in machine learning.pdf
machinelearning/Volume 02/#4/00 - New theoretical directions in machine learning.pdf
01 - Learning quickly when irrelevant attributes abound. A new linear-threshold algorithm.pdf
machinelearning/Volume 02/#4/01 - Learning quickly when irrelevant attributes abound. A new linear-threshold algorithm.pdf
02 - Queries and concept learning.pdf
machinelearning/Volume 02/#4/02 - Queries and concept learning.pdf
03 - Learning from noisy examples.pdf
machinelearning/Volume 02/#4/03 - Learning from noisy examples.pdf
04 - Criteria for polynomial-time (conceptual) clustering.pdf
machinelearning/Volume 02/#4/04 - Criteria for polynomial-time (conceptual) clustering.pdf
05 - News and notes.pdf
machinelearning/Volume 02/#4/05 - News and notes.pdf
06 - Announcing a new journal.pdf
machinelearning/Volume 02/#4/06 - Announcing a new journal.pdf
Volume 03
19 files • 15.42 MB
#1
4 files • 3.63 MB
00 - Machine learning as an experimental science.pdf
machinelearning/Volume 03/#1/00 - Machine learning as an experimental science.pdf
01 - Learning to predict by the methods of temporal differences.pdf
machinelearning/Volume 03/#1/01 - Learning to predict by the methods of temporal differences.pdf
02 - Learning by failing to explain. Using partial explanations to learn in incomplete or intractable domains.pdf
machinelearning/Volume 03/#1/02 - Learning by failing to explain. Using partial explanations to learn in incomplete or intractable domains.pdf
03 - A review of machine learning at AAAI-87.pdf
machinelearning/Volume 03/#1/03 - A review of machine learning at AAAI-87.pdf
#2-3
8 files • 6.54 MB
00 - Genetic algorithms and Machine Learning.pdf
machinelearning/Volume 03/#2-3/00 - Genetic algorithms and Machine Learning.pdf
01 - Genetic algorithms in noisy environments.pdf
machinelearning/Volume 03/#2-3/01 - Genetic algorithms in noisy environments.pdf
02 - Learning with genetic algorithms. An overview.pdf
machinelearning/Volume 03/#2-3/02 - Learning with genetic algorithms. An overview.pdf
03 - A tale of two classifier systems.pdf
machinelearning/Volume 03/#2-3/03 - A tale of two classifier systems.pdf
04 - Classifier systems that learn internal world models.pdf
machinelearning/Volume 03/#2-3/04 - Classifier systems that learn internal world models.pdf
05 - Learning and programming in classifier systems.pdf
machinelearning/Volume 03/#2-3/05 - Learning and programming in classifier systems.pdf
06 - Credit assignment in rule discovery systems based on genetic algorithms.pdf
machinelearning/Volume 03/#2-3/06 - Credit assignment in rule discovery systems based on genetic algorithms.pdf
07 - News and notes.pdf
machinelearning/Volume 03/#2-3/07 - News and notes.pdf
#4
7 files • 5.24 MB
00 - Toward a unified science of machine learning.pdf
machinelearning/Volume 03/#4/00 - Toward a unified science of machine learning.pdf
01 - The CN2 induction algorithm.pdf
machinelearning/Volume 03/#4/01 - The CN2 induction algorithm.pdf
02 - A heuristic approach to the discovery of macro-operators.pdf
machinelearning/Volume 03/#4/02 - A heuristic approach to the discovery of macro-operators.pdf
03 - An empirical comparison of selection measures for decision-tree induction.pdf
machinelearning/Volume 03/#4/03 - An empirical comparison of selection measures for decision-tree induction.pdf
04 - Conceptual clustering, categorization, and polymorphy.pdf
machinelearning/Volume 03/#4/04 - Conceptual clustering, categorization, and polymorphy.pdf
05 - News and notes.pdf
machinelearning/Volume 03/#4/05 - News and notes.pdf
06 - Erratum.pdf
machinelearning/Volume 03/#4/06 - Erratum.pdf
Volume 04
21 files • 16.96 MB
#1
6 files • 4.15 MB
00 - Editorial.pdf
machinelearning/Volume 04/#1/00 - Editorial.pdf
01 - Learning conjunctive concepts in structural domains.pdf
machinelearning/Volume 04/#1/01 - Learning conjunctive concepts in structural domains.pdf
02 - Semi-supervised learning.pdf
machinelearning/Volume 04/#1/02 - Semi-supervised learning.pdf
03 - On learning sets and functions.pdf
machinelearning/Volume 04/#1/03 - On learning sets and functions.pdf
04 - Efficient specialization of relational concepts.pdf
machinelearning/Volume 04/#1/04 - Efficient specialization of relational concepts.pdf
05 - News and notes.pdf
machinelearning/Volume 04/#1/05 - News and notes.pdf
#2
5 files • 6.31 MB
00 - Editorial.pdf
machinelearning/Volume 04/#2/00 - Editorial.pdf
01 - LT Revisited. Explanation-Based Learning and the Logic of Principia Mathematica.pdf
machinelearning/Volume 04/#2/01 - LT Revisited. Explanation-Based Learning and the Logic of Principia Mathematica.pdf
02 - Incremental Induction of Decision Trees.pdf
machinelearning/Volume 04/#2/02 - Incremental Induction of Decision Trees.pdf
03 - A Study of Explanation-Based Methods for Inductive Learning.pdf
machinelearning/Volume 04/#2/03 - A Study of Explanation-Based Methods for Inductive Learning.pdf
04 - An Empirical Comparison of Pruning Methods for Decision Tree Induction.pdf
machinelearning/Volume 04/#2/04 - An Empirical Comparison of Pruning Methods for Decision Tree Induction.pdf
#3-4
10 files • 6.5 MB
00 - Introduction. A sampler in knowledge acquisition for the machine learning community.pdf
machinelearning/Volume 04/#3-4/00 - Introduction. A sampler in knowledge acquisition for the machine learning community.pdf
01 - Can machine learning offer anything to expert systems.pdf
machinelearning/Volume 04/#3-4/01 - Can machine learning offer anything to expert systems.pdf
02 - When will machines learn.pdf
machinelearning/Volume 04/#3-4/02 - When will machines learn.pdf
03 - Supporting start-to-finish development of knowledge bases.pdf
machinelearning/Volume 04/#3-4/03 - Supporting start-to-finish development of knowledge bases.pdf
04 - The knowledge level reinterpreted. Modeling how systems interact.pdf
machinelearning/Volume 04/#3-4/04 - The knowledge level reinterpreted. Modeling how systems interact.pdf
05 - Automated knowledge acquisition for strategic knowledge.pdf
machinelearning/Volume 04/#3-4/05 - Automated knowledge acquisition for strategic knowledge.pdf
06 - The world would be a better place if non-programmers could program.pdf
machinelearning/Volume 04/#3-4/06 - The world would be a better place if non-programmers could program.pdf
07 - Task-structures, knowledge acquisition and learning.pdf
machinelearning/Volume 04/#3-4/07 - Task-structures, knowledge acquisition and learning.pdf
08 - Automated support for building and extending expert models.pdf
machinelearning/Volume 04/#3-4/08 - Automated support for building and extending expert models.pdf
09 - Knowledge acquisition for knowledge-based systems. Notes on the state-of-the-art.pdf
machinelearning/Volume 04/#3-4/09 - Knowledge acquisition for knowledge-based systems. Notes on the state-of-the-art.pdf
Volume 05
22 files • 17.59 MB
#1
6 files • 4.3 MB
00 - Editorial Exploratory research in machine learning.pdf
machinelearning/Volume 05/#1/00 - Editorial Exploratory research in machine learning.pdf
01 - Extending domain theories. Two case studies in student modeling.pdf
machinelearning/Volume 05/#1/01 - Extending domain theories. Two case studies in student modeling.pdf
02 - Acquiring recursive and iterative concepts with explanation-based learning.pdf
machinelearning/Volume 05/#1/02 - Acquiring recursive and iterative concepts with explanation-based learning.pdf
03 - Boolean feature discovery in empirical learning.pdf
machinelearning/Volume 05/#1/03 - Boolean feature discovery in empirical learning.pdf
04 - A necessary condition for learning from positive examples.pdf
machinelearning/Volume 05/#1/04 - A necessary condition for learning from positive examples.pdf
05 - Announcements.pdf
machinelearning/Volume 05/#1/05 - Announcements.pdf
#2
6 files • 4.47 MB
00 - Introduction. Special issue on computational learning theory.pdf
machinelearning/Volume 05/#2/00 - Introduction. Special issue on computational learning theory.pdf
01 - Negative results for equivalence queries.pdf
machinelearning/Volume 05/#2/01 - Negative results for equivalence queries.pdf
02 - Polynomial time learnability of simple deterministic languages.pdf
machinelearning/Volume 05/#2/02 - Polynomial time learnability of simple deterministic languages.pdf
03 - Learning nested differences of intersection-closed concept classes.pdf
machinelearning/Volume 05/#2/03 - Learning nested differences of intersection-closed concept classes.pdf
04 - The strength of weak learnability.pdf
machinelearning/Volume 05/#2/04 - The strength of weak learnability.pdf
05 - Errata.pdf
machinelearning/Volume 05/#2/05 - Errata.pdf
#3
4 files • 4.84 MB
00 - Editorial advice to Machine Learning authors.pdf
machinelearning/Volume 05/#3/00 - Editorial advice to Machine Learning authors.pdf
01 - Learning logical definitions from relations.pdf
machinelearning/Volume 05/#3/01 - Learning logical definitions from relations.pdf
02 - Empirical learning as a function of concept character.pdf
machinelearning/Volume 05/#3/02 - Empirical learning as a function of concept character.pdf
03 - The problem of expensive chunks and its solution by restricting expressiveness.pdf
machinelearning/Volume 05/#3/03 - The problem of expensive chunks and its solution by restricting expressiveness.pdf
#4
6 files • 3.98 MB
00 - Introduction.pdf
machinelearning/Volume 05/#4/00 - Introduction.pdf
01 - Learning sequential decision rules using simulation models and competition.pdf
machinelearning/Volume 05/#4/01 - Learning sequential decision rules using simulation models and competition.pdf
02 - CSM. A computational model of cumulative learning.pdf
machinelearning/Volume 05/#4/02 - CSM. A computational model of cumulative learning.pdf
03 - Probability matching, the magnitude of reinforcement, and classifier system bidding.pdf
machinelearning/Volume 05/#4/03 - Probability matching, the magnitude of reinforcement, and classifier system bidding.pdf
04 - Empirical learning using rule threshold optimization for detection of events in synthetic images.pdf
machinelearning/Volume 05/#4/04 - Empirical learning using rule threshold optimization for detection of events in synthetic images.pdf
05 - Call for papers for ICGA-91.pdf
machinelearning/Volume 05/#4/05 - Call for papers for ICGA-91.pdf
Volume 06
18 files • 13 MB
#1
8 files • 4.78 MB
00 - Editorial.pdf
machinelearning/Volume 06/#1/00 - Editorial.pdf
01 - Rigel. An Inductive Learning System.pdf
machinelearning/Volume 06/#1/01 - Rigel. An Inductive Learning System.pdf
02 - Instance-Based Learning Algorithms.pdf
machinelearning/Volume 06/#1/02 - Instance-Based Learning Algorithms.pdf
03 - Information-Based Evaluation Criterion for Classifier's Performance.pdf
machinelearning/Volume 06/#1/03 - Information-Based Evaluation Criterion for Classifier's Performance.pdf
04 - A Distance-Based Attribute Selection Measure for Decision Tree Induction.pdf
machinelearning/Volume 06/#1/04 - A Distance-Based Attribute Selection Measure for Decision Tree Induction.pdf
05 - Improved Estimates for the Accuracy of Small Disjuncts.pdf
machinelearning/Volume 06/#1/05 - Improved Estimates for the Accuracy of Small Disjuncts.pdf
06 - Book Review. Exemplar-Based Knowledge Acquisition, by Ray Bareiss. Academic Press, 1989.pdf
machinelearning/Volume 06/#1/06 - Book Review. Exemplar-Based Knowledge Acquisition, by Ray Bareiss. Academic Press, 1989.pdf
07 - A Reply To Reich's Book Review of Exemplar-Based Knowledge Acquisition.pdf
machinelearning/Volume 06/#1/07 - A Reply To Reich's Book Review of Exemplar-Based Knowledge Acquisition.pdf
#2
6 files • 4.2 MB
00 - Symbolic and neural learning algorithms. An experimental comparison.pdf
machinelearning/Volume 06/#2/00 - Symbolic and neural learning algorithms. An experimental comparison.pdf
01 - A PAC algorithm for making feature maps.pdf
machinelearning/Volume 06/#2/01 - A PAC algorithm for making feature maps.pdf
02 - Learning search control knowledge. An explanation-based approach.pdf
machinelearning/Volume 06/#2/02 - Learning search control knowledge. An explanation-based approach.pdf
02 - Letter recognition using Holland-style adaptive classifiers.pdf
machinelearning/Volume 06/#2/02 - Letter recognition using Holland-style adaptive classifiers.pdf
03 - A critical look at experimental evaluations of EBL.pdf
machinelearning/Volume 06/#2/03 - A critical look at experimental evaluations of EBL.pdf
04 - A reply to Zito-Wolf's book review ofLearning search control knowledge. An explanation-based approach.pdf
machinelearning/Volume 06/#2/04 - A reply to Zito-Wolf's book review ofLearning search control knowledge. An explanation-based approach.pdf
#3
4 files • 4.02 MB
00 - Complexity Results on Learning by Neural Nets.pdf
machinelearning/Volume 06/#3/00 - Complexity Results on Learning by Neural Nets.pdf
01 - The Use of Background Knowledge in Decision Tree Induction.pdf
machinelearning/Volume 06/#3/01 - The Use of Background Knowledge in Decision Tree Induction.pdf
02 - A Nearest Hyperrectangle Learning Method.pdf
machinelearning/Volume 06/#3/02 - A Nearest Hyperrectangle Learning Method.pdf
03 - Conflict Resolution as Discovery in Particle Physics.pdf
machinelearning/Volume 06/#3/03 - Conflict Resolution as Discovery in Particle Physics.pdf
Volume 07
10 files • 14.05 MB
#1
4 files • 4.15 MB
00 - Editorial.pdf
machinelearning/Volume 07/#1/00 - Editorial.pdf
01 - An incremental deductive strategy for controlling constructive induction in learning from examples.pdf
machinelearning/Volume 07/#1/01 - An incremental deductive strategy for controlling constructive induction in learning from examples.pdf
02 - Learning to perceive and act by trial and error.pdf
machinelearning/Volume 07/#1/02 - Learning to perceive and act by trial and error.pdf
03 - Learning to predict non-deterministically generated strings.pdf
machinelearning/Volume 07/#1/03 - Learning to predict non-deterministically generated strings.pdf
#2-3
6 files • 9.9 MB
00 - Introduction.pdf
machinelearning/Volume 07/#2-3/00 - Introduction.pdf
01 - Learning Automata from Ordered Examples.pdf
machinelearning/Volume 07/#2-3/01 - Learning Automata from Ordered Examples.pdf
02 - SLUG. A Connectionist Architecture for Inferring the Structure of Finite-State Environments.pdf
machinelearning/Volume 07/#2-3/02 - SLUG. A Connectionist Architecture for Inferring the Structure of Finite-State Environments.pdf
03 - Graded State Machines. The Representation of Temporal Contingencies in Simple Recurrent Networks.pdf
machinelearning/Volume 07/#2-3/03 - Graded State Machines. The Representation of Temporal Contingencies in Simple Recurrent Networks.pdf
04 - Distributed Representations, Simple Recurrent Networks, And Grammatical Structure.pdf
machinelearning/Volume 07/#2-3/04 - Distributed Representations, Simple Recurrent Networks, And Grammatical Structure.pdf
05 - The Induction of Dynamical Recognizers.pdf
machinelearning/Volume 07/#2-3/05 - The Induction of Dynamical Recognizers.pdf
Volume 08
17 files • 17.87 MB
#1
4 files • 4.6 MB
00 - Learning Two-Tiered Descriptions of Flexible Concepts. The POSEIDON System.pdf
machinelearning/Volume 08/#1/00 - Learning Two-Tiered Descriptions of Flexible Concepts. The POSEIDON System.pdf
01 - Implementing Valiant's Learnability Theory Using Random Sets.pdf
machinelearning/Volume 08/#1/01 - Implementing Valiant's Learnability Theory Using Random Sets.pdf
02 - A Further Comparison of Splitting Rules for Decision-Tree Induction.pdf
machinelearning/Volume 08/#1/02 - A Further Comparison of Splitting Rules for Decision-Tree Induction.pdf
03 - On the Handling of Continuous-Valued Attributes in Decision Tree Generation.pdf
machinelearning/Volume 08/#1/03 - On the Handling of Continuous-Valued Attributes in Decision Tree Generation.pdf
#2
5 files • 4.84 MB
00 - Editorial.pdf
machinelearning/Volume 08/#2/00 - Editorial.pdf
01 - Interactive concept-learning and constructive induction by analogy.pdf
machinelearning/Volume 08/#2/01 - Interactive concept-learning and constructive induction by analogy.pdf
02 - Learning probabilistic automata and Markov chains via queries.pdf
machinelearning/Volume 08/#2/02 - Learning probabilistic automata and Markov chains via queries.pdf
03 - Abductive explanation-based learning. A solution to the multiple inconsistent explanation problem.pdf
machinelearning/Volume 08/#2/03 - Abductive explanation-based learning. A solution to the multiple inconsistent explanation problem.pdf
04 - Announcement.pdf
machinelearning/Volume 08/#2/04 - Announcement.pdf
#3-4
8 files • 8.44 MB
00 - Introduction. The Challenge of Reinforcement Learning.pdf
machinelearning/Volume 08/#3-4/00 - Introduction. The Challenge of Reinforcement Learning.pdf
01 - Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.pdf
machinelearning/Volume 08/#3-4/01 - Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.pdf
02 - Practical Issues in Temporal Difference Learning.pdf
machinelearning/Volume 08/#3-4/02 - Practical Issues in Temporal Difference Learning.pdf
03 - Technical Note. Q-Learning.pdf
machinelearning/Volume 08/#3-4/03 - Technical Note. Q-Learning.pdf
04 - Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.pdf
machinelearning/Volume 08/#3-4/04 - Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.pdf
05 - Transfer of Learning by Composing Solutions of Elemental Sequential Tasks.pdf
machinelearning/Volume 08/#3-4/05 - Transfer of Learning by Composing Solutions of Elemental Sequential Tasks.pdf
06 - The Convergence of TD(lambda) for General lambda.pdf
machinelearning/Volume 08/#3-4/06 - The Convergence of TD(lambda) for General lambda.pdf
07 - A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments.pdf
machinelearning/Volume 08/#3-4/07 - A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments.pdf
Volume 09
17 files • 16.19 MB
#1
6 files • 4.19 MB
00 - Machine Learning. A Maturing Field.pdf
machinelearning/Volume 09/#1/00 - Machine Learning. A Maturing Field.pdf
01 - Dynamic Parameter Encoding for Genetic Algorithms.pdf
machinelearning/Volume 09/#1/01 - Dynamic Parameter Encoding for Genetic Algorithms.pdf
02 - Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization.pdf
machinelearning/Volume 09/#1/02 - Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization.pdf
03 - The Utility of Knowledge in Inductive Learning.pdf
machinelearning/Volume 09/#1/03 - The Utility of Knowledge in Inductive Learning.pdf
04 - Book Review. Neural Network Design and the Complexity of Learning, by J. Stephen Judd. Cambridge, MA. MIT Press, 1990.pdf
machinelearning/Volume 09/#1/04 - Book Review. Neural Network Design and the Complexity of Learning, by J. Stephen Judd. Cambridge, MA. MIT Press, 1990.pdf
05 - A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning.pdf
machinelearning/Volume 09/#1/05 - A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning.pdf
#2-3
6 files • 6.69 MB
00 - Introduction.pdf
machinelearning/Volume 09/#2-3/00 - Introduction.pdf
01 - Lower Bound Methods and Separation Results for On-Line Learning Models.pdf
machinelearning/Volume 09/#2-3/01 - Lower Bound Methods and Separation Results for On-Line Learning Models.pdf
02 - Learning Conjunctions of Horn Clauses.pdf
machinelearning/Volume 09/#2-3/02 - Learning Conjunctions of Horn Clauses.pdf
03 - A Learning Criterion for Stochastic Rules.pdf
machinelearning/Volume 09/#2-3/03 - A Learning Criterion for Stochastic Rules.pdf
04 - On the Computational Complexity of Approximating Distributions by Probabilistic Automata.pdf
machinelearning/Volume 09/#2-3/04 - On the Computational Complexity of Approximating Distributions by Probabilistic Automata.pdf
05 - A Universal Method of Scientific Inquiry.pdf
machinelearning/Volume 09/#2-3/05 - A Universal Method of Scientific Inquiry.pdf
#4
5 files • 5.32 MB
00 - Explorations of an Incremental, Bayesian Algorithm for Categorization.pdf
machinelearning/Volume 09/#4/00 - Explorations of an Incremental, Bayesian Algorithm for Categorization.pdf
01 - A Bayesian Method for the Induction of Probabilistic Networks from Data.pdf
machinelearning/Volume 09/#4/01 - A Bayesian Method for the Induction of Probabilistic Networks from Data.pdf
02 - A Framework for Average Case Analysis of Conjunctive Learning Algorithms.pdf
machinelearning/Volume 09/#4/02 - A Framework for Average Case Analysis of Conjunctive Learning Algorithms.pdf
03 - Learning Boolean Functions in an Infinite Attribute Space.pdf
machinelearning/Volume 09/#4/03 - Learning Boolean Functions in an Infinite Attribute Space.pdf
04 - Technical Note. First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem.pdf
machinelearning/Volume 09/#4/04 - Technical Note. First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem.pdf
Volume 10
14 files • 18.26 MB
#1
4 files • 5.7 MB
00 - Editorial.pdf
machinelearning/Volume 10/#1/00 - Editorial.pdf
01 - Synthesis of UNIX Programs Using Derivational Analogy.pdf
machinelearning/Volume 10/#1/01 - Synthesis of UNIX Programs Using Derivational Analogy.pdf
02 - A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features.pdf
machinelearning/Volume 10/#1/02 - A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features.pdf
03 - Induction Over the Unexplained. Using Overly-General Domain Theories to Aid Concept Learning.pdf
machinelearning/Volume 10/#1/03 - Induction Over the Unexplained. Using Overly-General Domain Theories to Aid Concept Learning.pdf
#2
4 files • 3.76 MB
00 - Information filtering. Selection mechanisms in learning systems.pdf
machinelearning/Volume 10/#2/00 - Information filtering. Selection mechanisms in learning systems.pdf
01 - Overfitting avoidance as bias.pdf
machinelearning/Volume 10/#2/01 - Overfitting avoidance as bias.pdf
02 - Creating a memory of causal relationships, by Michael Pazzani, Hillsdale, NJ. Lawrence Erlbaum, 1990.pdf
machinelearning/Volume 10/#2/02 - Creating a memory of causal relationships, by Michael Pazzani, Hillsdale, NJ. Lawrence Erlbaum, 1990.pdf
03 - A reply to Cohen's book review of Creating a Memory of Causal Relationships.pdf
machinelearning/Volume 10/#2/03 - A reply to Cohen's book review of Creating a Memory of Causal Relationships.pdf
#3
6 files • 8.8 MB
00 - Introduction.pdf
machinelearning/Volume 10/#3/00 - Introduction.pdf
01 - Indexing, Elaboration and Refinement. Incremental Learning of Explanatory Cases.pdf
machinelearning/Volume 10/#3/01 - Indexing, Elaboration and Refinement. Incremental Learning of Explanatory Cases.pdf
02 - Derivational Analogy in PRODIGY. Automating Case Acquisition, Storage, and Utilization.pdf
machinelearning/Volume 10/#3/02 - Derivational Analogy in PRODIGY. Automating Case Acquisition, Storage, and Utilization.pdf
03 - Opportunism and Learning.pdf
machinelearning/Volume 10/#3/03 - Opportunism and Learning.pdf
04 - Integrating Feature Extraction and Memory Search.pdf
machinelearning/Volume 10/#3/04 - Integrating Feature Extraction and Memory Search.pdf
05 - Wastewater Treatment Systems from Case–Based Reasoning.pdf
machinelearning/Volume 10/#3/05 - Wastewater Treatment Systems from Case–Based Reasoning.pdf
Volume 11
12 files • 12.14 MB
#1
5 files • 3.98 MB
00 - Coding Decision Trees.pdf
machinelearning/Volume 11/#1/00 - Coding Decision Trees.pdf
01 - Active Learning Using Arbitrary Binary Valued Queries.pdf
machinelearning/Volume 11/#1/01 - Active Learning Using Arbitrary Binary Valued Queries.pdf
02 - Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees.pdf
machinelearning/Volume 11/#1/02 - Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees.pdf
03 - Very Simple Classification Rules Perform Well on Most Commonly Used Datasets.pdf
machinelearning/Volume 11/#1/03 - Very Simple Classification Rules Perform Well on Most Commonly Used Datasets.pdf
04 - An Analysis of the WITT Algorithm.pdf
machinelearning/Volume 11/#1/04 - An Analysis of the WITT Algorithm.pdf
#2-3
7 files • 8.16 MB
00 - Introduction.pdf
machinelearning/Volume 11/#2-3/00 - Introduction.pdf
01 - Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning.pdf
machinelearning/Volume 11/#2-3/01 - Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning.pdf
03 - Multistrategy Learning and Theory Revision.pdf
machinelearning/Volume 11/#2-3/03 - Multistrategy Learning and Theory Revision.pdf
04 - Learning Causal Patterns. Making a Transition from Data-Driven to Theory-Driven Learning.pdf
machinelearning/Volume 11/#2-3/04 - Learning Causal Patterns. Making a Transition from Data-Driven to Theory-Driven Learning.pdf
05 - Using Knowledge-Based Neural Networks to Improve Algorithms. Refining the Chou–Fasman Algorithm for Protein Folding.pdf
machinelearning/Volume 11/#2-3/05 - Using Knowledge-Based Neural Networks to Improve Algorithms. Refining the Chou–Fasman Algorithm for Protein Folding.pdf
06 - Balanced Cooperative Modeling.pdf
machinelearning/Volume 11/#2-3/06 - Balanced Cooperative Modeling.pdf
07 - Plausible Justification Trees. A Framework for Deep and Dynamic Integration of Learning Strategies.pdf
machinelearning/Volume 11/#2-3/07 - Plausible Justification Trees. A Framework for Deep and Dynamic Integration of Learning Strategies.pdf
Volume 12
10 files • 8.84 MB
#1-3
10 files • 8.84 MB
00 - Introduction. Cognitive autonomy in machine discovery.pdf
machinelearning/Volume 12/#1-3/00 - Introduction. Cognitive autonomy in machine discovery.pdf
01 - An integrated framework for empirical discovery.pdf
machinelearning/Volume 12/#1-3/01 - An integrated framework for empirical discovery.pdf
02 - Experience selection and problem choice in an exploratory learning system.pdf
machinelearning/Volume 12/#1-3/02 - Experience selection and problem choice in an exploratory learning system.pdf
03 - Discovery by Minimal Length Encoding. A case study in molecular evolution.pdf
machinelearning/Volume 12/#1-3/03 - Discovery by Minimal Length Encoding. A case study in molecular evolution.pdf
04 - Design methods for scientific hypothesis formation and their application to molecular biology.pdf
machinelearning/Volume 12/#1-3/04 - Design methods for scientific hypothesis formation and their application to molecular biology.pdf
05 - Machine discovery of effective admissible heuristics.pdf
machinelearning/Volume 12/#1-3/05 - Machine discovery of effective admissible heuristics.pdf
06 - Discovery as autonomous learning from the environment.pdf
machinelearning/Volume 12/#1-3/06 - Discovery as autonomous learning from the environment.pdf
07 - Bivariate scientific function finding in a sampled, real-data testbed.pdf
machinelearning/Volume 12/#1-3/07 - Bivariate scientific function finding in a sampled, real-data testbed.pdf
08 - The design of discrimination experiments.pdf
machinelearning/Volume 12/#1-3/08 - The design of discrimination experiments.pdf
09 - Publisher's note.pdf
machinelearning/Volume 12/#1-3/09 - Publisher's note.pdf
Volume 13
13 files • 16.51 MB
#1
7 files • 7.36 MB
00 - Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics.pdf
machinelearning/Volume 13/#1/00 - Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics.pdf
01 - Explanation-Based Learning for Diagnosis.pdf
machinelearning/Volume 13/#1/01 - Explanation-Based Learning for Diagnosis.pdf
02 - Extracting Refined Rules from Knowledge-Based Neural Networks.pdf
machinelearning/Volume 13/#1/02 - Extracting Refined Rules from Knowledge-Based Neural Networks.pdf
03 - Prioritized Sweeping. Reinforcement Learning with Less Data and Less Time.pdf
machinelearning/Volume 13/#1/03 - Prioritized Sweeping. Reinforcement Learning with Less Data and Less Time.pdf
04 - Technical Note. Selecting a Classification Method by Cross-Validation.pdf
machinelearning/Volume 13/#1/04 - Technical Note. Selecting a Classification Method by Cross-Validation.pdf
05 - Book Review Machine Learning. A Theoretical Approach by Balas K. Natarajan. Morgan Kaufmann Publishers, Inc., 1991.pdf
machinelearning/Volume 13/#1/05 - Book Review Machine Learning. A Theoretical Approach by Balas K. Natarajan. Morgan Kaufmann Publishers, Inc., 1991.pdf
06 - A Reply to Hellerstein's Book Review of Machine Learning. A Theoretical Approach.pdf
machinelearning/Volume 13/#1/06 - A Reply to Hellerstein's Book Review of Machine Learning. A Theoretical Approach.pdf
#2-3
6 files • 9.15 MB
00 - Introduction.pdf
machinelearning/Volume 13/#2-3/00 - Introduction.pdf
01 - Using Genetic Algorithms for Concept Learning.pdf
machinelearning/Volume 13/#2-3/01 - Using Genetic Algorithms for Concept Learning.pdf
02 - A Knowledge-Intensive Genetic Algorithm for Supervised Learning.pdf
machinelearning/Volume 13/#2-3/02 - A Knowledge-Intensive Genetic Algorithm for Supervised Learning.pdf
03 - Competition-Based Induction of Decision Models from Examples.pdf
machinelearning/Volume 13/#2-3/03 - Competition-Based Induction of Decision Models from Examples.pdf
04 - Genetic Reinforcement Learning for Neurocontrol Problems.pdf
machinelearning/Volume 13/#2-3/04 - Genetic Reinforcement Learning for Neurocontrol Problems.pdf
05 - What Makes a Problem Hard for a Genetic Algorithm Some Anomalous Results and Their Explanation.pdf
machinelearning/Volume 13/#2-3/05 - What Makes a Problem Hard for a Genetic Algorithm Some Anomalous Results and Their Explanation.pdf
Volume 14
20 files • 15.41 MB
#1
6 files • 5.05 MB
00 - Foreword.pdf
machinelearning/Volume 14/#1/00 - Foreword.pdf
01 - Randomly Fallible Teachers. Learning Monotone DNF with an Incomplete Membership Oracle.pdf
machinelearning/Volume 14/#1/01 - Randomly Fallible Teachers. Learning Monotone DNF with an Incomplete Membership Oracle.pdf
02 - Tracking Drifting Concepts By Minimizing Disagreements.pdf
machinelearning/Volume 14/#1/02 - Tracking Drifting Concepts By Minimizing Disagreements.pdf
03 - Learning Probabilistic Read-once Formulas on Product Distributions.pdf
machinelearning/Volume 14/#1/03 - Learning Probabilistic Read-once Formulas on Product Distributions.pdf
04 - Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension.pdf
machinelearning/Volume 14/#1/04 - Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension.pdf
05 - Approximation and Estimation Bounds for Artificial Neural Networks.pdf
machinelearning/Volume 14/#1/05 - Approximation and Estimation Bounds for Artificial Neural Networks.pdf
#2
6 files • 6.26 MB
00 - Guest Editorial.pdf
machinelearning/Volume 14/#2/00 - Guest Editorial.pdf
01 - Hypothesis-Driven Constructive Induction in AQ17-HCI. A Method and Experiments.pdf
machinelearning/Volume 14/#2/01 - Hypothesis-Driven Constructive Induction in AQ17-HCI. A Method and Experiments.pdf
02 - Concept Formation During Interactive Theory Revision.pdf
machinelearning/Volume 14/#2/02 - Concept Formation During Interactive Theory Revision.pdf
03 - A Polynomial Approach to the Constructive Induction of Structural Knowledge.pdf
machinelearning/Volume 14/#2/03 - A Polynomial Approach to the Constructive Induction of Structural Knowledge.pdf
04 - Flattening and Saturation. Two Representation Changes for Generalization.pdf
machinelearning/Volume 14/#2/04 - Flattening and Saturation. Two Representation Changes for Generalization.pdf
05 - Explicit Representation of Concept Negation.pdf
machinelearning/Volume 14/#2/05 - Explicit Representation of Concept Negation.pdf
#3
8 files • 4.09 MB
00 - Algorithms and lower bounds for on-line learning of geometrical concepts.pdf
machinelearning/Volume 14/#3/00 - Algorithms and lower bounds for on-line learning of geometrical concepts.pdf
01 - The power of self-directed learning.pdf
machinelearning/Volume 14/#3/01 - The power of self-directed learning.pdf
02 - TD(lambda) converges with probability 1.pdf
machinelearning/Volume 14/#3/02 - TD(lambda) converges with probability 1.pdf
03 - Introduction to the abstracts of the invited talks presented at ML92 conference in Aberdeen, 1–3 July 1992.pdf
machinelearning/Volume 14/#3/03 - Introduction to the abstracts of the invited talks presented at ML92 conference in Aberdeen, 1–3 July 1992.pdf
04 - Machine learning and qualitative reasoning.pdf
machinelearning/Volume 14/#3/04 - Machine learning and qualitative reasoning.pdf
05 - Children, adults, and machines as discovery systems.pdf
machinelearning/Volume 14/#3/05 - Children, adults, and machines as discovery systems.pdf
06 - Combining symbolic and neural learning.pdf
machinelearning/Volume 14/#3/06 - Combining symbolic and neural learning.pdf
07 - Statistical methods for analyzing speedup learning experiments.pdf
machinelearning/Volume 14/#3/07 - Statistical methods for analyzing speedup learning experiments.pdf
Volume 15
14 files • 15.84 MB
#1
4 files • 5.49 MB
00 - Incremental Abductive EBL.pdf
machinelearning/Volume 15/#1/00 - Incremental Abductive EBL.pdf
01 - The Importance of Attribute Selection Measures in Decision Tree Induction.pdf
machinelearning/Volume 15/#1/01 - The Importance of Attribute Selection Measures in Decision Tree Induction.pdf
02 - Discrete Sequence Prediction and Its Applications.pdf
machinelearning/Volume 15/#1/02 - Discrete Sequence Prediction and Its Applications.pdf
03 - On-Line Learning from Search Failures.pdf
machinelearning/Volume 15/#1/03 - On-Line Learning from Search Failures.pdf
#2
5 files • 5.93 MB
00 - Introduction. Structured Connectionist Systems.pdf
machinelearning/Volume 15/#2/00 - Introduction. Structured Connectionist Systems.pdf
01 - Neural Network-Based Vision for Precise Control of a Walking Robot.pdf
machinelearning/Volume 15/#2/01 - Neural Network-Based Vision for Precise Control of a Walking Robot.pdf
02 - Using Neural Networks to Modularize Software.pdf
machinelearning/Volume 15/#2/02 - Using Neural Networks to Modularize Software.pdf
03 - Higher-Order Neural Networks Applied to 2D and 3D Object Recognition.pdf
machinelearning/Volume 15/#2/03 - Higher-Order Neural Networks Applied to 2D and 3D Object Recognition.pdf
04 - Improving Generalization with Active Learning.pdf
machinelearning/Volume 15/#2/04 - Improving Generalization with Active Learning.pdf
#3
5 files • 4.42 MB
00 - Trading accuracy for simplicity in decision trees.pdf
machinelearning/Volume 15/#3/00 - Trading accuracy for simplicity in decision trees.pdf
01 - Toward an ideal trainer.pdf
machinelearning/Volume 15/#3/01 - Toward an ideal trainer.pdf
02 - Associative reinforcement learning. Functions in k-DNF.pdf
machinelearning/Volume 15/#3/02 - Associative reinforcement learning. Functions in k-DNF.pdf
03 - Associative reinforcement learning. A generate and test algorithm.pdf
machinelearning/Volume 15/#3/03 - Associative reinforcement learning. A generate and test algorithm.pdf
04 - Bias in information-based measures in decision tree induction.pdf
machinelearning/Volume 15/#3/04 - Bias in information-based measures in decision tree induction.pdf
Volume 16
12 files • 10.85 MB
#1-2
7 files • 7.75 MB
00 - Editorial. New Editorial Board Members.pdf
machinelearning/Volume 16/#1-2/00 - Editorial. New Editorial Board Members.pdf
01 - Guest Editor's Introduction.pdf
machinelearning/Volume 16/#1-2/01 - Guest Editor's Introduction.pdf
02 - Acquisition of Children's Addition Strategies. A Model of Impasse-Free, Knowledge-Level Learning.pdf
machinelearning/Volume 16/#1-2/02 - Acquisition of Children's Addition Strategies. A Model of Impasse-Free, Knowledge-Level Learning.pdf
03 - Case-Based Learning. Predictive Features in Indexing.pdf
machinelearning/Volume 16/#1-2/03 - Case-Based Learning. Predictive Features in Indexing.pdf
04 - Modeling Cognitive Development on Balance Scale Phenomena.pdf
machinelearning/Volume 16/#1-2/04 - Modeling Cognitive Development on Balance Scale Phenomena.pdf
05 - Simulating the Child's Acquisition of the Lexicon and Syntax - Experiences With Babel.pdf
machinelearning/Volume 16/#1-2/05 - Simulating the Child's Acquisition of the Lexicon and Syntax - Experiences With Babel.pdf
06 - Acquiring and Combining Overlapping Concepts.pdf
machinelearning/Volume 16/#1-2/06 - Acquiring and Combining Overlapping Concepts.pdf
#3
5 files • 3.1 MB
00 - Learning nonoverlapping perceptron networks from examples and membership queries.pdf
machinelearning/Volume 16/#3/00 - Learning nonoverlapping perceptron networks from examples and membership queries.pdf
01 - Asynchronous stochastic approximation and Q-learning.pdf
machinelearning/Volume 16/#3/01 - Asynchronous stochastic approximation and Q-learning.pdf
02 - Complexity-based induction.pdf
machinelearning/Volume 16/#3/02 - Complexity-based induction.pdf
03 - An upper bound on the loss from approximate optimal-value functions.pdf
machinelearning/Volume 16/#3/03 - An upper bound on the loss from approximate optimal-value functions.pdf
04 - C4.5. Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993.pdf
machinelearning/Volume 16/#3/04 - C4.5. Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993.pdf
Volume 17
8 files • 8.9 MB
#1
3 files • 4.34 MB
00 - Generalizing Version Spaces.pdf
machinelearning/Volume 17/#1/00 - Generalizing Version Spaces.pdf
01 - Algebraic Reasoning about Reactions. Discovery of Conserved Properties in Particle Physics.pdf
machinelearning/Volume 17/#1/01 - Algebraic Reasoning about Reactions. Discovery of Conserved Properties in Particle Physics.pdf
02 - Quantifying Prior Determination Knowledge Using the PAC Learning Model.pdf
machinelearning/Volume 17/#1/02 - Quantifying Prior Determination Knowledge Using the PAC Learning Model.pdf
#2-3
5 files • 4.56 MB
00 - Guest editor's introduction.pdf
machinelearning/Volume 17/#2-3/00 - Guest editor's introduction.pdf
01 - Toward efficient agnostic learning.pdf
machinelearning/Volume 17/#2-3/01 - Toward efficient agnostic learning.pdf
02 - A theory for memory-based learning.pdf
machinelearning/Volume 17/#2-3/02 - A theory for memory-based learning.pdf
03 - The learnability of description logics with equality constraints.pdf
machinelearning/Volume 17/#2-3/03 - The learnability of description logics with equality constraints.pdf
04 - On-line learning of rectangles and unions of rectangles.pdf
machinelearning/Volume 17/#2-3/04 - On-line learning of rectangles and unions of rectangles.pdf
Volume 18
13 files • 12.32 MB
#1
7 files • 5.2 MB
00 - A Branch and Bound Incremental Conceptual Clusterer.pdf
machinelearning/Volume 18/#1/00 - A Branch and Bound Incremental Conceptual Clusterer.pdf
01 - Probably Almost Discriminative Learning.pdf
machinelearning/Volume 18/#1/01 - Probably Almost Discriminative Learning.pdf
02 - A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping.pdf
machinelearning/Volume 18/#1/02 - A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping.pdf
03 - Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning.pdf
machinelearning/Volume 18/#1/03 - Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning.pdf
04 - Classification Accuracy. Machine Learning vs. Explicit Knowledge Acquisition.pdf
machinelearning/Volume 18/#1/04 - Classification Accuracy. Machine Learning vs. Explicit Knowledge Acquisition.pdf
05 - Book Review. Neural Network Perception For Mobile Robot Guidanceby Dean A. Pomerleau. Kluwer Academic Publishers, 1993.pdf
machinelearning/Volume 18/#1/05 - Book Review. Neural Network Perception For Mobile Robot Guidanceby Dean A. Pomerleau. Kluwer Academic Publishers, 1993.pdf
06 - A Reply to Towell's Book Review of Neural Network Perception for Mobile Robot Guidance.pdf
machinelearning/Volume 18/#1/06 - A Reply to Towell's Book Review of Neural Network Perception for Mobile Robot Guidance.pdf
#2-3
6 files • 7.12 MB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 18/#2-3/00 - Guest Editor's Introduction.pdf
01 - Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers.pdf
machinelearning/Volume 18/#2-3/01 - Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers.pdf
02 - Learning Fallible Deterministic Finite Automata.pdf
machinelearning/Volume 18/#2-3/02 - Learning Fallible Deterministic Finite Automata.pdf
03 - On the Complexity of Function Learning.pdf
machinelearning/Volume 18/#2-3/03 - On the Complexity of Function Learning.pdf
04 - Piecemeal Learning of an Unknown Environment.pdf
machinelearning/Volume 18/#2-3/04 - Piecemeal Learning of an Unknown Environment.pdf
05 - Learning from a Population of Hypotheses.pdf
machinelearning/Volume 18/#2-3/05 - Learning from a Population of Hypotheses.pdf
Volume 19
10 files • 12.98 MB
#1
4 files • 5 MB
00 - An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms.pdf
machinelearning/Volume 19/#1/00 - An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms.pdf
01 - Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms.pdf
machinelearning/Volume 19/#1/01 - Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms.pdf
02 - Multivariate Decision Trees.pdf
machinelearning/Volume 19/#1/02 - Multivariate Decision Trees.pdf
03 - Encouraging Experimental Results on Learning CNF.pdf
machinelearning/Volume 19/#1/03 - Encouraging Experimental Results on Learning CNF.pdf
#2
3 files • 3.55 MB
00 - Automated Refinement of First-Order Horn-Clause Domain Theories.pdf
machinelearning/Volume 19/#2/00 - Automated Refinement of First-Order Horn-Clause Domain Theories.pdf
01 - Comprehension Grammars Generated from Machine Learning of Natural Languages.pdf
machinelearning/Volume 19/#2/01 - Comprehension Grammars Generated from Machine Learning of Natural Languages.pdf
02 - On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata.pdf
machinelearning/Volume 19/#2/02 - On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata.pdf
#3
3 files • 4.44 MB
00 - On the Learnability of Disjunctive Normal Form Formulas.pdf
machinelearning/Volume 19/#3/00 - On the Learnability of Disjunctive Normal Form Formulas.pdf
01 - ALECSYS and the AutonoMouse. Learning to Control a Real Robot by Distributed Classifier Systems.pdf
machinelearning/Volume 19/#3/01 - ALECSYS and the AutonoMouse. Learning to Control a Real Robot by Distributed Classifier Systems.pdf
02 - On the Stochastic Complexity of Learning Realizable and Unrealizable Rules.pdf
machinelearning/Volume 19/#3/02 - On the Stochastic Complexity of Learning Realizable and Unrealizable Rules.pdf
Volume 20
10 files • 14.16 MB
#1-2
7 files • 9.53 MB
00 - Evaluation and Selection of Biases in Machine Learning.pdf
machinelearning/Volume 20/#1-2/00 - Evaluation and Selection of Biases in Machine Learning.pdf
01 - Technical Note. Bias and the Quantification of Stability.pdf
machinelearning/Volume 20/#1-2/01 - Technical Note. Bias and the Quantification of Stability.pdf
02 - Inductive Policy. The Pragmatics of Bias Selection.pdf
machinelearning/Volume 20/#1-2/02 - Inductive Policy. The Pragmatics of Bias Selection.pdf
03 - Recursive Automatic Bias Selection for Classifier Construction.pdf
machinelearning/Volume 20/#1-2/03 - Recursive Automatic Bias Selection for Classifier Construction.pdf
04 - The Appropriateness of Predicate Invention as Bias Shift Operation in ILP.pdf
machinelearning/Volume 20/#1-2/04 - The Appropriateness of Predicate Invention as Bias Shift Operation in ILP.pdf
05 - Declarative Bias for Specific-to-General ILP Systems.pdf
machinelearning/Volume 20/#1-2/05 - Declarative Bias for Specific-to-General ILP Systems.pdf
06 - Shifting Vocabulary Bias in Speedup Learning.pdf
machinelearning/Volume 20/#1-2/06 - Shifting Vocabulary Bias in Speedup Learning.pdf
#3
3 files • 4.63 MB
00 - Learning Bayesian Networks. The Combination of Knowledge and Statistical Data.pdf
machinelearning/Volume 20/#3/00 - Learning Bayesian Networks. The Combination of Knowledge and Statistical Data.pdf
01 - Learning Binary Relations Using Weighted Majority Voting.pdf
machinelearning/Volume 20/#3/01 - Learning Binary Relations Using Weighted Majority Voting.pdf
02 - Support-Vector Networks.pdf
machinelearning/Volume 20/#3/02 - Support-Vector Networks.pdf
Volume 21
12 files • 13.99 MB
#1-2
9 files • 8.65 MB
00 - Introduction.pdf
machinelearning/Volume 21/#1-2/00 - Introduction.pdf
01 - Genetic Algorithms, Operators, and DNA Fragment Assembly.pdf
machinelearning/Volume 21/#1-2/01 - Genetic Algorithms, Operators, and DNA Fragment Assembly.pdf
02 - Discovering Dependencies via Algorithmic Mutual Information. A Case Study in DNA Sequence Comparisons.pdf
machinelearning/Volume 21/#1-2/02 - Discovering Dependencies via Algorithmic Mutual Information. A Case Study in DNA Sequence Comparisons.pdf
03 - Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization.pdf
machinelearning/Volume 21/#1-2/03 - Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization.pdf
04 - DEXTER. A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration.pdf
machinelearning/Volume 21/#1-2/04 - DEXTER. A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration.pdf
05 - Use of Adaptive Networks to Define Highly Predictable Protein Secondary–Structure Classes.pdf
machinelearning/Volume 21/#1-2/05 - Use of Adaptive Networks to Define Highly Predictable Protein Secondary–Structure Classes.pdf
06 - Machine Discovery of Protein Motifs.pdf
machinelearning/Volume 21/#1-2/06 - Machine Discovery of Protein Motifs.pdf
07 - Searching for Representations to Improve Protein Sequence Fold-Class Prediction.pdf
machinelearning/Volume 21/#1-2/07 - Searching for Representations to Improve Protein Sequence Fold-Class Prediction.pdf
08 - Neural Networks for Full-Scale Protein Sequence Classification. Sequence Encoding with Singular Value Decomposition.pdf
machinelearning/Volume 21/#1-2/08 - Neural Networks for Full-Scale Protein Sequence Classification. Sequence Encoding with Singular Value Decomposition.pdf
#3
3 files • 5.34 MB
00 - The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces.pdf
machinelearning/Volume 21/#3/00 - The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces.pdf
01 - An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts.pdf
machinelearning/Volume 21/#3/01 - An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts.pdf
02 - Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.pdf
machinelearning/Volume 21/#3/02 - Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.pdf
Volume 22
12 files • 10.86 MB
#1-3
12 files • 10.86 MB
00 - Editorial.pdf
machinelearning/Volume 22/#1-3/00 - Editorial.pdf
01 - Introduction.pdf
machinelearning/Volume 22/#1-3/01 - Introduction.pdf
02 - Efficient reinforcement learning through symbiotic evolution.pdf
machinelearning/Volume 22/#1-3/02 - Efficient reinforcement learning through symbiotic evolution.pdf
03 - Linear Least-Squares algorithms for temporal difference learning.pdf
machinelearning/Volume 22/#1-3/03 - Linear Least-Squares algorithms for temporal difference learning.pdf
04 - Feature-based methods for large scale dynamic programming.pdf
machinelearning/Volume 22/#1-3/04 - Feature-based methods for large scale dynamic programming.pdf
05 - On the worst-case analysis of temporal-difference learning algorithms.pdf
machinelearning/Volume 22/#1-3/05 - On the worst-case analysis of temporal-difference learning algorithms.pdf
06 - Reinforcement learning with replacing eligibility traces.pdf
machinelearning/Volume 22/#1-3/06 - Reinforcement learning with replacing eligibility traces.pdf
07 - Average reward reinforcement learning. Foundations, algorithms, and empirical results.pdf
machinelearning/Volume 22/#1-3/07 - Average reward reinforcement learning. Foundations, algorithms, and empirical results.pdf
08 - The loss from imperfect value functions in expectation-based and minimax-based tasks.pdf
machinelearning/Volume 22/#1-3/08 - The loss from imperfect value functions in expectation-based and minimax-based tasks.pdf
09 - The effect of representation and knowledge on goal-directed exploration with reinforcement-learning algorithms.pdf
machinelearning/Volume 22/#1-3/09 - The effect of representation and knowledge on goal-directed exploration with reinforcement-learning algorithms.pdf
10 - Creating advice-taking reinforcement learners.pdf
machinelearning/Volume 22/#1-3/10 - Creating advice-taking reinforcement learners.pdf
11 - Incremental multi-step Q-learning.pdf
machinelearning/Volume 22/#1-3/11 - Incremental multi-step Q-learning.pdf
Volume 23
14 files • 11.7 MB
#1
6 files • 3.16 MB
00 - Representation of Finite State Automata in Recurrent Radial Basis Function Networks.pdf
machinelearning/Volume 23/#1/00 - Representation of Finite State Automata in Recurrent Radial Basis Function Networks.pdf
01 - Scaling Up Inductive Learning with Massive Parallelism.pdf
machinelearning/Volume 23/#1/01 - Scaling Up Inductive Learning with Massive Parallelism.pdf
02 - Classification by Feature Partitioning.pdf
machinelearning/Volume 23/#1/02 - Classification by Feature Partitioning.pdf
03 - Learning in the Presence of Concept Drift and Hidden Contexts.pdf
machinelearning/Volume 23/#1/03 - Learning in the Presence of Concept Drift and Hidden Contexts.pdf
04 - Review of Inductive Logic Programming. Techniques and Applications by Nada Lavrac, Saso Dzeroski.pdf
machinelearning/Volume 23/#1/04 - Review of Inductive Logic Programming. Techniques and Applications by Nada Lavrac, Saso Dzeroski.pdf
05 - A Reply to Pazzani's Book Review of Inductive Logic Programming. Techniques and Applications.pdf
machinelearning/Volume 23/#1/05 - A Reply to Pazzani's Book Review of Inductive Logic Programming. Techniques and Applications.pdf
#2-3
8 files • 8.54 MB
00 - Introduction.pdf
machinelearning/Volume 23/#2-3/00 - Introduction.pdf
01 - Real-world robotics. Learning to plan for robust execution.pdf
machinelearning/Volume 23/#2-3/01 - Real-world robotics. Learning to plan for robust execution.pdf
02 - Robot programming by Demonstration (RPD). Supporting the induction by human interaction.pdf
machinelearning/Volume 23/#2-3/02 - Robot programming by Demonstration (RPD). Supporting the induction by human interaction.pdf
03 - Performance improvement of robot continuous-path operation through iterative learning using neural networks.pdf
machinelearning/Volume 23/#2-3/03 - Performance improvement of robot continuous-path operation through iterative learning using neural networks.pdf
04 - Learning controllers for industrial robots.pdf
machinelearning/Volume 23/#2-3/04 - Learning controllers for industrial robots.pdf
05 - Active learning for vision-based robot grasping.pdf
machinelearning/Volume 23/#2-3/05 - Active learning for vision-based robot grasping.pdf
06 - Purposive behavior acquisition for a real robot by vision-based reinforcement learning.pdf
machinelearning/Volume 23/#2-3/06 - Purposive behavior acquisition for a real robot by vision-based reinforcement learning.pdf
07 - Learning concepts from sensor data of a mobile robot.pdf
machinelearning/Volume 23/#2-3/07 - Learning concepts from sensor data of a mobile robot.pdf
Volume 24
10 files • 3.78 MB
#1
4 files • 2.09 MB
00 - BEXA. A Covering Algorithm for Learning Propositional Concept Descriptions.pdf
machinelearning/Volume 24/#1/00 - BEXA. A Covering Algorithm for Learning Propositional Concept Descriptions.pdf
01 - Technical Note. Some Properties of Splitting Criteria.pdf
machinelearning/Volume 24/#1/01 - Technical Note. Some Properties of Splitting Criteria.pdf
02 - Stacked Regressions.pdf
machinelearning/Volume 24/#1/02 - Stacked Regressions.pdf
03 - On Learning Visual Concepts and DNF Formulae.pdf
machinelearning/Volume 24/#1/03 - On Learning Visual Concepts and DNF Formulae.pdf
#2
3 files • 948.2 KB
00 - A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval.pdf
machinelearning/Volume 24/#2/00 - A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval.pdf
01 - Bagging Predictors.pdf
machinelearning/Volume 24/#2/01 - Bagging Predictors.pdf
02 - Unifying Instance-Based and Rule-Based Induction.pdf
machinelearning/Volume 24/#2/02 - Unifying Instance-Based and Rule-Based Induction.pdf
#3
3 files • 783.56 KB
00 - Error Reduction through Learning Multiple Descriptions.pdf
machinelearning/Volume 24/#3/00 - Error Reduction through Learning Multiple Descriptions.pdf
01 - A Decision-Tree Model of Balance Scale Development.pdf
machinelearning/Volume 24/#3/01 - A Decision-Tree Model of Balance Scale Development.pdf
02 - Efficient Incremental Induction of Decision Trees.pdf
machinelearning/Volume 24/#3/02 - Efficient Incremental Induction of Decision Trees.pdf
Volume 25
9 files • 8.06 MB
#1
4 files • 1.63 MB
00 - Exploration Bonuses and Dual Control.pdf
machinelearning/Volume 25/#1/00 - Exploration Bonuses and Dual Control.pdf
01 - On-line Prediction and Conversion Strategies.pdf
machinelearning/Volume 25/#1/01 - On-line Prediction and Conversion Strategies.pdf
01 - Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs.pdf
machinelearning/Volume 25/#1/01 - Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs.pdf
02 - PAC Learning of One-Dimensional Patterns.pdf
machinelearning/Volume 25/#1/02 - PAC Learning of One-Dimensional Patterns.pdf
#2-3
5 files • 6.43 MB
00 - Guest editor's introduction.pdf
machinelearning/Volume 25/#2-3/00 - Guest editor's introduction.pdf
01 - The power of amnesia. Learning probabilistic automata with variable memory length.pdf
machinelearning/Volume 25/#2-3/01 - The power of amnesia. Learning probabilistic automata with variable memory length.pdf
02 - Classic learning.pdf
machinelearning/Volume 25/#2-3/02 - Classic learning.pdf
03 - Rigorous learning curve bounds from statistical mechanics.pdf
machinelearning/Volume 25/#2-3/03 - Rigorous learning curve bounds from statistical mechanics.pdf
04 - On the limits of proper learnability of subclasses of DNF formulas.pdf
machinelearning/Volume 25/#2-3/04 - On the limits of proper learnability of subclasses of DNF formulas.pdf
Volume 26
10 files • 2.84 MB
#1
4 files • 943.4 KB
00 - Empirical Support for Winnow and Weighted-Majority Algorithms Results on a Calendar Scheduling Domain.pdf
machinelearning/Volume 26/#1/00 - Empirical Support for Winnow and Weighted-Majority Algorithms Results on a Calendar Scheduling Domain.pdf
01 - Exact Learning of Formulas in Parallel.pdf
machinelearning/Volume 26/#1/01 - Exact Learning of Formulas in Parallel.pdf
02 - Learning and Updating of Uncertainty in Dirichlet Models.pdf
machinelearning/Volume 26/#1/02 - Learning and Updating of Uncertainty in Dirichlet Models.pdf
03 - A Microscopic Study of Minimum Entropy Search in Learning Decomposable Markov Networks.pdf
machinelearning/Volume 26/#1/03 - A Microscopic Study of Minimum Entropy Search in Learning Decomposable Markov Networks.pdf
#2-3
6 files • 1.91 MB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 26/#2-3/00 - Guest Editors' Introduction.pdf
01 - Clausal Discovery.pdf
machinelearning/Volume 26/#2-3/01 - Clausal Discovery.pdf
02 - First Order Regression.pdf
machinelearning/Volume 26/#2-3/02 - First Order Regression.pdf
03 - Learning Qualitative Models of Dynamic Systems.pdf
machinelearning/Volume 26/#2-3/03 - Learning Qualitative Models of Dynamic Systems.pdf
04 - Generalization of Clauses Relative to a Theory.pdf
machinelearning/Volume 26/#2-3/04 - Generalization of Clauses Relative to a Theory.pdf
05 - A Pattern-Based First-Order Inductive System.pdf
machinelearning/Volume 26/#2-3/05 - A Pattern-Based First-Order Inductive System.pdf
Volume 27
14 files • 4.18 MB
#1
5 files • 2.02 MB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 27/#1/00 - Guest Editor's Introduction.pdf
01 - An Experimental and Theoretical Comparison of Model Selection Methods.pdf
machinelearning/Volume 27/#1/01 - An Experimental and Theoretical Comparison of Model Selection Methods.pdf
02 - Predicting Nearly As Well As the Best Pruning of a Decision Tree.pdf
machinelearning/Volume 27/#1/02 - Predicting Nearly As Well As the Best Pruning of a Decision Tree.pdf
03 - Exactly Learning Automata of Small Cover Time.pdf
machinelearning/Volume 27/#1/03 - Exactly Learning Automata of Small Cover Time.pdf
04 - A Comparison of New and Old Algorithms for a Mixture Estimation Problem.pdf
machinelearning/Volume 27/#1/04 - A Comparison of New and Old Algorithms for a Mixture Estimation Problem.pdf
#2
3 files • 820.76 KB
00 - Characteristic Sets for Polynomial Grammatical Inference.pdf
machinelearning/Volume 27/#2/00 - Characteristic Sets for Polynomial Grammatical Inference.pdf
01 - Pruning Algorithms for Rule Learning.pdf
machinelearning/Volume 27/#2/01 - Pruning Algorithms for Rule Learning.pdf
02 - Representing Probabilistic Rules with Networks of Gaussian Basis Functions.pdf
machinelearning/Volume 27/#2/02 - Representing Probabilistic Rules with Networks of Gaussian Basis Functions.pdf
#3
6 files • 1.36 MB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 27/#3/00 - Guest Editors' Introduction.pdf
01 - Integrating Multiple Learning Strategies in First Order Logics.pdf
machinelearning/Volume 27/#3/01 - Integrating Multiple Learning Strategies in First Order Logics.pdf
02 - Using Background Knowledge to Build Multistrategy Learners.pdf
machinelearning/Volume 27/#3/02 - Using Background Knowledge to Build Multistrategy Learners.pdf
03 - Tracking Context Changes through Meta-Learning.pdf
machinelearning/Volume 27/#3/03 - Tracking Context Changes through Meta-Learning.pdf
04 - A Multistrategy Approach to Relational Knowledge Discovery in Databases.pdf
machinelearning/Volume 27/#3/04 - A Multistrategy Approach to Relational Knowledge Discovery in Databases.pdf
05 - Learning and Revising User Profiles The Identification of Interesting Web Sites.pdf
machinelearning/Volume 27/#3/05 - Learning and Revising User Profiles The Identification of Interesting Web Sites.pdf
Volume 28
9 files • 3.42 MB
#1
5 files • 1.45 MB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 28/#1/00 - Guest Editors' Introduction.pdf
01 - A Bayesian-Information Theoretic Model of Learning to Learn via Multiple Task Sampling.pdf
machinelearning/Volume 28/#1/01 - A Bayesian-Information Theoretic Model of Learning to Learn via Multiple Task Sampling.pdf
02 - Multitask Learning.pdf
machinelearning/Volume 28/#1/02 - Multitask Learning.pdf
03 - CHILD A First Step Towards Continual Learning.pdf
machinelearning/Volume 28/#1/03 - CHILD A First Step Towards Continual Learning.pdf
04 - Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement.pdf
machinelearning/Volume 28/#1/04 - Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement.pdf
#2-3
4 files • 1.96 MB
00 - Selective Sampling Using the Query by Committee Algorithm.pdf
machinelearning/Volume 28/#2-3/00 - Selective Sampling Using the Query by Committee Algorithm.pdf
01 - Explanation-Based Learning and Reinforcement Learning A Unified View.pdf
machinelearning/Volume 28/#2-3/01 - Explanation-Based Learning and Reinforcement Learning A Unified View.pdf
02 - Malicious Omissions and Errors in Answers to Membership Queries.pdf
machinelearning/Volume 28/#2-3/02 - Malicious Omissions and Errors in Answers to Membership Queries.pdf
03 - An Exact Probability Metric for Decision Tree Splitting and Stopping.pdf
machinelearning/Volume 28/#2-3/03 - An Exact Probability Metric for Decision Tree Splitting and Stopping.pdf
Volume 29
11 files • 3.88 MB
#1
3 files • 1.1 MB
00 - Decision Tree Induction Based on Efficient Tree Restructuring.pdf
machinelearning/Volume 29/#1/00 - Decision Tree Induction Based on Efficient Tree Restructuring.pdf
01 - Online Learning versus Offline Learning.pdf
machinelearning/Volume 29/#1/01 - Online Learning versus Offline Learning.pdf
02 - Coping with Uncertainty in Map Learning.pdf
machinelearning/Volume 29/#1/02 - Coping with Uncertainty in Map Learning.pdf
#2-3
8 files • 2.78 MB
00 - Learning with Probabilistic Representations.pdf
machinelearning/Volume 29/#2-3/00 - Learning with Probabilistic Representations.pdf
01 - On the Optimality of the Simple Bayesian Classifier under Zero-One Loss.pdf
machinelearning/Volume 29/#2-3/01 - On the Optimality of the Simple Bayesian Classifier under Zero-One Loss.pdf
02 - Bayesian Network Classifiers.pdf
machinelearning/Volume 29/#2-3/02 - Bayesian Network Classifiers.pdf
03 - The Sample Complexity of Learning Fixed-Structure Bayesian Networks.pdf
machinelearning/Volume 29/#2-3/03 - The Sample Complexity of Learning Fixed-Structure Bayesian Networks.pdf
04 - Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables.pdf
machinelearning/Volume 29/#2-3/04 - Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables.pdf
05 - Adaptive Probabilistic Networks with Hidden Variables.pdf
machinelearning/Volume 29/#2-3/05 - Adaptive Probabilistic Networks with Hidden Variables.pdf
06 - Factorial Hidden Markov Models.pdf
machinelearning/Volume 29/#2-3/06 - Factorial Hidden Markov Models.pdf
07 - Predicting Protein Secondary Structure Using Stochastic Tree Grammars.pdf
machinelearning/Volume 29/#2-3/07 - Predicting Protein Secondary Structure Using Stochastic Tree Grammars.pdf
Volume 30
13 files • 4.73 MB
#1
6 files • 1.72 MB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 30/#1/00 - Guest Editor's Introduction.pdf
01 - PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.pdf
machinelearning/Volume 30/#1/01 - PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.pdf
02 - A Note on Learning from Multiple-Instance Examples.pdf
machinelearning/Volume 30/#1/02 - A Note on Learning from Multiple-Instance Examples.pdf
03 - Strong Minimax Lower Bounds for Learning.pdf
machinelearning/Volume 30/#1/03 - Strong Minimax Lower Bounds for Learning.pdf
04 - Hardness Results for Learning First-Order Representations and Programming by Demonstration.pdf
machinelearning/Volume 30/#1/04 - Hardness Results for Learning First-Order Representations and Programming by Demonstration.pdf
05 - On Restricted-Focus-of-Attention Learnability of Boolean Functions.pdf
machinelearning/Volume 30/#1/05 - On Restricted-Focus-of-Attention Learnability of Boolean Functions.pdf
#2-3
7 files • 3 MB
00 - Guest Editors' Introduction On Applied Research in Machine Learning.pdf
machinelearning/Volume 30/#2-3/00 - Guest Editors' Introduction On Applied Research in Machine Learning.pdf
01 - Learning in the Real World.pdf
machinelearning/Volume 30/#2-3/01 - Learning in the Real World.pdf
02 - Learning to Recognize Volcanoes on Venus.pdf
machinelearning/Volume 30/#2-3/02 - Learning to Recognize Volcanoes on Venus.pdf
03 - Machine Learning for the Detection of Oil Spills in Satellite Radar Images.pdf
machinelearning/Volume 30/#2-3/03 - Machine Learning for the Detection of Oil Spills in Satellite Radar Images.pdf
04 - Knowledge-Based Learning in Exploratory Science Learning Rules to Predict Rodent Carcinogenicity.pdf
machinelearning/Volume 30/#2-3/04 - Knowledge-Based Learning in Exploratory Science Learning Rules to Predict Rodent Carcinogenicity.pdf
05 - Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL.pdf
machinelearning/Volume 30/#2-3/05 - Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL.pdf
06 - Glossary of Terms.pdf
machinelearning/Volume 30/#2-3/06 - Glossary of Terms.pdf
Volume 31
12 files • 2.5 MB
#1-3
12 files • 2.5 MB
00 - Foreword.pdf
machinelearning/Volume 31/#1-3/00 - Foreword.pdf
01 - Rapid Concept Learning for Mobile Robots.pdf
machinelearning/Volume 31/#1-3/01 - Rapid Concept Learning for Mobile Robots.pdf
02 - A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots.pdf
machinelearning/Volume 31/#1-3/02 - A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots.pdf
03 - Module-Based Reinforcement Learning - Experiments with a Real Robot.pdf
machinelearning/Volume 31/#1-3/03 - Module-Based Reinforcement Learning - Experiments with a Real Robot.pdf
04 - Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory.pdf
machinelearning/Volume 31/#1-3/04 - Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory.pdf
05 - Learning from Innate Behaviors - A Quantitative Evaluation of Neural Network Controllers.pdf
machinelearning/Volume 31/#1-3/05 - Learning from Innate Behaviors - A Quantitative Evaluation of Neural Network Controllers.pdf
06 - Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions.pdf
machinelearning/Volume 31/#1-3/06 - Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions.pdf
07 - Self Calibration of the Fixation Movement of a Stereo Camera Head.pdf
machinelearning/Volume 31/#1-3/07 - Self Calibration of the Fixation Movement of a Stereo Camera Head.pdf
08 - Emergence and Categorization of Coordinated Visual Behavior Through Embodied Interaction.pdf
machinelearning/Volume 31/#1-3/08 - Emergence and Categorization of Coordinated Visual Behavior Through Embodied Interaction.pdf
09 - Training a Vision Guided Mobile Robot.pdf
machinelearning/Volume 31/#1-3/09 - Training a Vision Guided Mobile Robot.pdf
10 - Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy.pdf
machinelearning/Volume 31/#1-3/10 - Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy.pdf
11 - Learning to Recognize and Grasp Objects.pdf
machinelearning/Volume 31/#1-3/11 - Learning to Recognize and Grasp Objects.pdf
Volume 32
14 files • 4.32 MB
#1
4 files • 2.15 MB
00 - Analytical Mean Squared Error Curves for Temporal Difference Learning.pdf
machinelearning/Volume 32/#1/00 - Analytical Mean Squared Error Curves for Temporal Difference Learning.pdf
01 - The Hierarchical Hidden Markov Model Analysis and Applications.pdf
machinelearning/Volume 32/#1/01 - The Hierarchical Hidden Markov Model Analysis and Applications.pdf
02 - Using Model Trees for Classification.pdf
machinelearning/Volume 32/#1/02 - Using Model Trees for Classification.pdf
03 - Erratum.pdf
machinelearning/Volume 32/#1/03 - Erratum.pdf
#2
6 files • 1.42 MB
00 - Guest Editors'Introduction.pdf
machinelearning/Volume 32/#2/00 - Guest Editors'Introduction.pdf
01 - Robust Sensor Fusion Analysis and Application to Audio Visual Speech Recognition.pdf
machinelearning/Volume 32/#2/01 - Robust Sensor Fusion Analysis and Application to Audio Visual Speech Recognition.pdf
02 - Extracting Hidden Context.pdf
machinelearning/Volume 32/#2/02 - Extracting Hidden Context.pdf
03 - Tracking the Best Disjunction.pdf
machinelearning/Volume 32/#2/03 - Tracking the Best Disjunction.pdf
04 - Tracking the Best Expert.pdf
machinelearning/Volume 32/#2/04 - Tracking the Best Expert.pdf
05 - Statistical Mechanics of Online Learning of Drifting Concepts A Variational Approach.pdf
machinelearning/Volume 32/#2/05 - Statistical Mechanics of Online Learning of Drifting Concepts A Variational Approach.pdf
#3
4 files • 760.97 KB
00 - Localization vs. Identification of Semi-Algebraic Sets.pdf
machinelearning/Volume 32/#3/00 - Localization vs. Identification of Semi-Algebraic Sets.pdf
01 - Co-Evolution in the Successful Learning of Backgammon Strategy.pdf
machinelearning/Volume 32/#3/01 - Co-Evolution in the Successful Learning of Backgammon Strategy.pdf
02 - Comments on “Co-Evolution in the Successful Learning of Backgammon Strategy”.pdf
machinelearning/Volume 32/#3/02 - Comments on “Co-Evolution in the Successful Learning of Backgammon Strategy”.pdf
03 - Learning from Examples and Membership Queries with Structured Determinations.pdf
machinelearning/Volume 32/#3/03 - Learning from Examples and Membership Queries with Structured Determinations.pdf
Volume 33
12 files • 2.34 MB
#1
5 files • 947.57 KB
00 - Prequential and Cross-Validated Regression Estimation.pdf
machinelearning/Volume 33/#1/00 - Prequential and Cross-Validated Regression Estimation.pdf
01 - Bayesian Landmark Learning for Mobile Robot Localization.pdf
machinelearning/Volume 33/#1/01 - Bayesian Landmark Learning for Mobile Robot Localization.pdf
02 - Choice of Basis for Laplace Approximation.pdf
machinelearning/Volume 33/#1/02 - Choice of Basis for Laplace Approximation.pdf
03 - Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning.pdf
machinelearning/Volume 33/#1/03 - Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning.pdf
04 - Fast Online Q(lambda).pdf
machinelearning/Volume 33/#1/04 - Fast Online Q(lambda).pdf
#2-3
7 files • 1.42 MB
00 - Guest Editorial.pdf
machinelearning/Volume 33/#2-3/00 - Guest Editorial.pdf
01 - Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments.pdf
machinelearning/Volume 33/#2-3/01 - Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments.pdf
02 - Learning Coordination Strategies for Cooperative Multiagent Systems.pdf
machinelearning/Volume 33/#2-3/02 - Learning Coordination Strategies for Cooperative Multiagent Systems.pdf
03 - Conjectural Equilibrium in Multiagent Learning.pdf
machinelearning/Volume 33/#2-3/03 - Conjectural Equilibrium in Multiagent Learning.pdf
04 - Colearning in Differential Games.pdf
machinelearning/Volume 33/#2-3/04 - Colearning in Differential Games.pdf
05 - Elevator Group Control Using Multiple Reinforcement Learning Agents.pdf
machinelearning/Volume 33/#2-3/05 - Elevator Group Control Using Multiple Reinforcement Learning Agents.pdf
06 - Learning Team Strategies Soccer Case Studies.pdf
machinelearning/Volume 33/#2-3/06 - Learning Team Strategies Soccer Case Studies.pdf
Volume 34
10 files • 2.24 MB
#1-3
10 files • 2.24 MB
00 - Guest Editors' Introduction Machine Learning and Natural Language.pdf
machinelearning/Volume 34/#1-3/00 - Guest Editors' Introduction Machine Learning and Natural Language.pdf
01 - Forgetting Exceptions is Harmful in Language Learning.pdf
machinelearning/Volume 34/#1-3/01 - Forgetting Exceptions is Harmful in Language Learning.pdf
02 - Similarity-Based Models of Word Cooccurrence Probabilities.pdf
machinelearning/Volume 34/#1-3/02 - Similarity-Based Models of Word Cooccurrence Probabilities.pdf
03 - An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery.pdf
machinelearning/Volume 34/#1-3/03 - An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery.pdf
04 - A Winnow-Based Approach to Context-Sensitive Spelling Correction.pdf
machinelearning/Volume 34/#1-3/04 - A Winnow-Based Approach to Context-Sensitive Spelling Correction.pdf
05 - Using Decision Trees to Construct a Practical Parser.pdf
machinelearning/Volume 34/#1-3/05 - Using Decision Trees to Construct a Practical Parser.pdf
06 - Learning to Parse Natural Language with Maximum Entropy Models.pdf
machinelearning/Volume 34/#1-3/06 - Learning to Parse Natural Language with Maximum Entropy Models.pdf
07 - Statistical Models for Text Segmentation.pdf
machinelearning/Volume 34/#1-3/07 - Statistical Models for Text Segmentation.pdf
08 - An Algorithm that Learns What's in a Name.pdf
machinelearning/Volume 34/#1-3/08 - An Algorithm that Learns What's in a Name.pdf
09 - Learning Information Extraction Rules for Semi-Structured and Free Text.pdf
machinelearning/Volume 34/#1-3/09 - Learning Information Extraction Rules for Semi-Structured and Free Text.pdf
Volume 35
12 files • 1.5 MB
#1
4 files • 453.11 KB
00 - An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition.pdf
machinelearning/Volume 35/#1/00 - An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition.pdf
01 - Concept Learning and Feature Selection Based on Square-Error Clustering.pdf
machinelearning/Volume 35/#1/01 - Concept Learning and Feature Selection Based on Square-Error Clustering.pdf
02 - An Efficient Method To Estimate Bagging's Generalization Error.pdf
machinelearning/Volume 35/#1/02 - An Efficient Method To Estimate Bagging's Generalization Error.pdf
03 - Learning to Take Actions.pdf
machinelearning/Volume 35/#1/03 - Learning to Take Actions.pdf
#2
3 files • 627.86 KB
00 - Learning to Reason with a Restricted View.pdf
machinelearning/Volume 35/#2/00 - Learning to Reason with a Restricted View.pdf
01 - Exploration of Multi-State Environments Local Measures and Back-Propagation of Uncertainty.pdf
machinelearning/Volume 35/#2/01 - Exploration of Multi-State Environments Local Measures and Back-Propagation of Uncertainty.pdf
02 - Toward a Model of Intelligence as an Economy of Agents.pdf
machinelearning/Volume 35/#2/02 - Toward a Model of Intelligence as an Economy of Agents.pdf
#3
5 files • 453.07 KB
00 - Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT '97.pdf
machinelearning/Volume 35/#3/00 - Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT '97.pdf
01 - Universal Portfolios With and Without Transaction Costs.pdf
machinelearning/Volume 35/#3/01 - Universal Portfolios With and Without Transaction Costs.pdf
02 - A Dichotomy Theorem for Learning Quantified Boolean Formulas.pdf
machinelearning/Volume 35/#3/02 - A Dichotomy Theorem for Learning Quantified Boolean Formulas.pdf
03 - Estimation of Time-Varying Parameters in Statistical Models An Optimization Approach.pdf
machinelearning/Volume 35/#3/03 - Estimation of Time-Varying Parameters in Statistical Models An Optimization Approach.pdf
04 - Derandomizing Stochastic Prediction Strategies.pdf
machinelearning/Volume 35/#3/04 - Derandomizing Stochastic Prediction Strategies.pdf
Volume 36
9 files • 1.69 MB
#1-2
6 files • 1.03 MB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 36/#1-2/00 - Guest Editors' Introduction.pdf
01 - A Principal Components Approach to Combining Regression Estimates.pdf
machinelearning/Volume 36/#1-2/01 - A Principal Components Approach to Combining Regression Estimates.pdf
02 - Using Correspondence Analysis to Combine Classifiers.pdf
machinelearning/Volume 36/#1-2/02 - Using Correspondence Analysis to Combine Classifiers.pdf
03 - Linearly Combining Density Estimators via Stacking.pdf
machinelearning/Volume 36/#1-2/03 - Linearly Combining Density Estimators via Stacking.pdf
04 - Pasting Small Votes for Classification in Large Databases and On-Line.pdf
machinelearning/Volume 36/#1-2/04 - Pasting Small Votes for Classification in Large Databases and On-Line.pdf
05 - An Empirical Comparison of Voting Classification Algorithms Bagging, Boosting, and Variants.pdf
machinelearning/Volume 36/#1-2/05 - An Empirical Comparison of Voting Classification Algorithms Bagging, Boosting, and Variants.pdf
#3
3 files • 673.98 KB
00 - Structural Results About On-line Learning Models With and Without Queries.pdf
machinelearning/Volume 36/#3/00 - Structural Results About On-line Learning Models With and Without Queries.pdf
01 - An Efficient Extension to Mixture Techniques for Prediction and Decision Trees.pdf
machinelearning/Volume 36/#3/01 - An Efficient Extension to Mixture Techniques for Prediction and Decision Trees.pdf
02 - General and Efficient Multisplitting of Numerical Attributes.pdf
machinelearning/Volume 36/#3/02 - General and Efficient Multisplitting of Numerical Attributes.pdf
Volume 37
14 files • 2.76 MB
#1
4 files • 1.15 MB
00 - A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns.pdf
machinelearning/Volume 37/#1/00 - A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns.pdf
01 - Minimum Generalization Via Reflection A Fast Linear Threshold Learner.pdf
machinelearning/Volume 37/#1/01 - Minimum Generalization Via Reflection A Fast Linear Threshold Learner.pdf
02 - Mixed Memory Markov Models Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones.pdf
machinelearning/Volume 37/#1/02 - Mixed Memory Markov Models Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones.pdf
03 - Efficient Read-Restricted Monotone CNF-DNF Dualization by Learning with Membership Queries.pdf
machinelearning/Volume 37/#1/03 - Efficient Read-Restricted Monotone CNF-DNF Dualization by Learning with Membership Queries.pdf
#2
4 files • 940.96 KB
00 - Projection Learning.pdf
machinelearning/Volume 37/#2/00 - Projection Learning.pdf
01 - On the Sample Complexity for Nonoverlapping Neural Networks.pdf
machinelearning/Volume 37/#2/01 - On the Sample Complexity for Nonoverlapping Neural Networks.pdf
02 - Effective and Efficient Knowledge Base Refinement.pdf
machinelearning/Volume 37/#2/02 - Effective and Efficient Knowledge Base Refinement.pdf
03 - An Introduction to Variational Methods for Graphical Models.pdf
machinelearning/Volume 37/#2/03 - An Introduction to Variational Methods for Graphical Models.pdf
#3
6 files • 711.11 KB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 37/#3/00 - Guest Editors' Introduction.pdf
01 - Learning Function-Free Horn Expressions.pdf
machinelearning/Volume 37/#3/01 - Learning Function-Free Horn Expressions.pdf
02 - Large Margin Classification Using the Perceptron Algorithm.pdf
machinelearning/Volume 37/#3/02 - Large Margin Classification Using the Perceptron Algorithm.pdf
03 - Improved Boosting Algorithms Using Confidence-rated Predictions.pdf
machinelearning/Volume 37/#3/03 - Improved Boosting Algorithms Using Confidence-rated Predictions.pdf
04 - The Complexity of Learning According to Two Models of a Drifting Environment.pdf
machinelearning/Volume 37/#3/04 - The Complexity of Learning According to Two Models of a Drifting Environment.pdf
05 - Some PAC-Bayesian Theorems.pdf
machinelearning/Volume 37/#3/05 - Some PAC-Bayesian Theorems.pdf
Volume 38
14 files • 2.66 MB
#1-2
10 files • 1.98 MB
00 - Guest Editors' Introduction.pdf
machinelearning/Volume 38/#1-2/00 - Guest Editors' Introduction.pdf
01 - Evolutionary Processes Guided by Machine Learning.pdf
machinelearning/Volume 38/#1-2/01 - Evolutionary Processes Guided by Machine Learning.pdf
02 - Resource-bounded Relational Reasoning Induction and Deduction Through Stochastic Matching.pdf
machinelearning/Volume 38/#1-2/02 - Resource-bounded Relational Reasoning Induction and Deduction Through Stochastic Matching.pdf
03 - Strategies in Combined Learning via Logic Programs.pdf
machinelearning/Volume 38/#1-2/03 - Strategies in Combined Learning via Logic Programs.pdf
04 - Feature Selection vs Theory Reformulation A Study of Genetic Refinement of Knowledge-based Neural Networks.pdf
machinelearning/Volume 38/#1-2/04 - Feature Selection vs Theory Reformulation A Study of Genetic Refinement of Knowledge-based Neural Networks.pdf
05 - Refining Numerical Constants in First Order Logic Theories.pdf
machinelearning/Volume 38/#1-2/05 - Refining Numerical Constants in First Order Logic Theories.pdf
06 - Multistrategy Theory Revision Induction and Abduction in INTHELEX.pdf
machinelearning/Volume 38/#1-2/06 - Multistrategy Theory Revision Induction and Abduction in INTHELEX.pdf
07 - Multistrategy Discovery and Detection of Novice Programmer Errors.pdf
machinelearning/Volume 38/#1-2/07 - Multistrategy Discovery and Detection of Novice Programmer Errors.pdf
08 - Multi Level Knowledge in Modeling Qualitative Physics Learning.pdf
machinelearning/Volume 38/#1-2/08 - Multi Level Knowledge in Modeling Qualitative Physics Learning.pdf
09 - A Multistrategy Approach to Classifier Learning from Time Series.pdf
machinelearning/Volume 38/#1-2/09 - A Multistrategy Approach to Classifier Learning from Time Series.pdf
#3
4 files • 696 KB
00 - Improved Generalization Through Explicit Optimization of Margins.pdf
machinelearning/Volume 38/#3/00 - Improved Generalization Through Explicit Optimization of Margins.pdf
01 - Reduction Techniques for Instance-Based Learning Algorithms.pdf
machinelearning/Volume 38/#3/01 - Reduction Techniques for Instance-Based Learning Algorithms.pdf
02 - Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms.pdf
machinelearning/Volume 38/#3/02 - Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms.pdf
03 - Multiple Comparisons in Induction Algorithms.pdf
machinelearning/Volume 38/#3/03 - Multiple Comparisons in Induction Algorithms.pdf
Volume 39
8 files • 1.56 MB
#1
3 files • 490.83 KB
00 - Nonparametric Time Series Prediction Through Adaptive Model Selection.pdf
machinelearning/Volume 39/#1/00 - Nonparametric Time Series Prediction Through Adaptive Model Selection.pdf
01 - On-line Learning and the Metrical Task System Problem.pdf
machinelearning/Volume 39/#1/01 - On-line Learning and the Metrical Task System Problem.pdf
02 - A Machine Learning Approach to POS Tagging.pdf
machinelearning/Volume 39/#1/02 - A Machine Learning Approach to POS Tagging.pdf
#2-3
5 files • 1.08 MB
00 - Special Issue of Machine Learning on Information Retrieval Introduction.pdf
machinelearning/Volume 39/#2-3/00 - Special Issue of Machine Learning on Information Retrieval Introduction.pdf
01 - Text Classification from Labeled and Unlabeled Documents using EM.pdf
machinelearning/Volume 39/#2-3/01 - Text Classification from Labeled and Unlabeled Documents using EM.pdf
02 - BoosTexter A Boosting-based System for Text Categorization.pdf
machinelearning/Volume 39/#2-3/02 - BoosTexter A Boosting-based System for Text Categorization.pdf
03 - Machine Learning for Information Extraction in Informal Domains.pdf
machinelearning/Volume 39/#2-3/03 - Machine Learning for Information Extraction in Informal Domains.pdf
04 - Adaptive Retrieval Agents Internalizing Local Context and Scaling up to the Web.pdf
machinelearning/Volume 39/#2-3/04 - Adaptive Retrieval Agents Internalizing Local Context and Scaling up to the Web.pdf
Volume 40
10 files • 2.18 MB
#1
3 files • 922.9 KB
00 - Implementation Issues in the Fourier Transform Algorithm.pdf
machinelearning/Volume 40/#1/00 - Implementation Issues in the Fourier Transform Algorithm.pdf
01 - Constructing X-of-N Attributes for Decision Tree Learning.pdf
machinelearning/Volume 40/#1/01 - Constructing X-of-N Attributes for Decision Tree Learning.pdf
02 - Upper and Lower Bounds on the Learning Curve for Gaussian Processes.pdf
machinelearning/Volume 40/#1/02 - Upper and Lower Bounds on the Learning Curve for Gaussian Processes.pdf
#2
3 files • 642.31 KB
00 - Stochastic Grammatical Inference of Text Database Structure.pdf
machinelearning/Volume 40/#2/00 - Stochastic Grammatical Inference of Text Database Structure.pdf
01 - An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees Bagging, Boosting, and Randomization.pdf
machinelearning/Volume 40/#2/01 - An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees Bagging, Boosting, and Randomization.pdf
02 - MultiBoosting A Technique for Combining Boosting and Wagging.pdf
machinelearning/Volume 40/#2/02 - MultiBoosting A Technique for Combining Boosting and Wagging.pdf
#3
4 files • 670.01 KB
00 - A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms.pdf
machinelearning/Volume 40/#3/00 - A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms.pdf
01 - Randomizing Outputs to Increase Prediction Accuracy.pdf
machinelearning/Volume 40/#3/01 - Randomizing Outputs to Increase Prediction Accuracy.pdf
02 - Learning to Play Chess Using Temporal Differences.pdf
machinelearning/Volume 40/#3/02 - Learning to Play Chess Using Temporal Differences.pdf
03 - A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions.pdf
machinelearning/Volume 40/#3/03 - A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions.pdf
Volume 41
13 files • 2.9 MB
#1
4 files • 1005.6 KB
00 - Technical Note Naive Bayes for Regression.pdf
machinelearning/Volume 41/#1/00 - Technical Note Naive Bayes for Regression.pdf
01 - Selecting Examples for Partial Memory Learning.pdf
machinelearning/Volume 41/#1/01 - Selecting Examples for Partial Memory Learning.pdf
02 - Lazy Learning of Bayesian Rules.pdf
machinelearning/Volume 41/#1/02 - Lazy Learning of Bayesian Rules.pdf
03 - A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners.pdf
machinelearning/Volume 41/#1/03 - A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners.pdf
#2
5 files • 749.89 KB
00 - Maximizing Theory Accuracy Through Selective Reinterpretation.pdf
machinelearning/Volume 41/#2/00 - Maximizing Theory Accuracy Through Selective Reinterpretation.pdf
01 - Learning Changing Concepts by Exploiting the Structure of Change.pdf
machinelearning/Volume 41/#2/01 - Learning Changing Concepts by Exploiting the Structure of Change.pdf
02 - A Formalism for Relevance and Its Application in Feature Subset Selection.pdf
machinelearning/Volume 41/#2/02 - A Formalism for Relevance and Its Application in Feature Subset Selection.pdf
03 - Adaptive Versus Nonadaptive Attribute-Efficient Learning.pdf
machinelearning/Volume 41/#2/03 - Adaptive Versus Nonadaptive Attribute-Efficient Learning.pdf
04 - Phase Transitions in Relational Learning.pdf
machinelearning/Volume 41/#2/04 - Phase Transitions in Relational Learning.pdf
#3
4 files • 1.19 MB
00 - Bottom-Up Induction of Feature Terms.pdf
machinelearning/Volume 41/#3/00 - Bottom-Up Induction of Feature Terms.pdf
01 - Enlarging the Margins in Perceptron Decision Trees.pdf
machinelearning/Volume 41/#3/01 - Enlarging the Margins in Perceptron Decision Trees.pdf
02 - Cascade Generalization.pdf
machinelearning/Volume 41/#3/02 - Cascade Generalization.pdf
03 - Markov Processes on Curves.pdf
machinelearning/Volume 41/#3/03 - Markov Processes on Curves.pdf
Volume 42
14 files • 2.12 MB
#1-2
8 files • 1.36 MB
00 - Introduction.pdf
machinelearning/Volume 42/#1-2/00 - Introduction.pdf
01 - An Experimental Comparison of Model-Based Clustering Methods.pdf
machinelearning/Volume 42/#1-2/01 - An Experimental Comparison of Model-Based Clustering Methods.pdf
02 - SPADE An Efficient Algorithm for Mining Frequent Sequences.pdf
machinelearning/Volume 42/#1-2/02 - SPADE An Efficient Algorithm for Mining Frequent Sequences.pdf
03 - Confirmation-Guided Discovery of First-Order Rules with Tertius.pdf
machinelearning/Volume 42/#1-2/03 - Confirmation-Guided Discovery of First-Order Rules with Tertius.pdf
04 - Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks.pdf
machinelearning/Volume 42/#1-2/04 - Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks.pdf
05 - Linear Concepts and Hidden Variables.pdf
machinelearning/Volume 42/#1-2/05 - Linear Concepts and Hidden Variables.pdf
06 - Concept Decompositions for Large Sparse Text Data Using Clustering.pdf
machinelearning/Volume 42/#1-2/06 - Concept Decompositions for Large Sparse Text Data Using Clustering.pdf
07 - Unsupervised Learning by Probabilistic Latent Semantic Analysis.pdf
machinelearning/Volume 42/#1-2/07 - Unsupervised Learning by Probabilistic Latent Semantic Analysis.pdf
#3
6 files • 778.27 KB
00 - Robust Classification for Imprecise Environments.pdf
machinelearning/Volume 42/#3/00 - Robust Classification for Imprecise Environments.pdf
01 - A Learning Generalization Bound with an Application to Sparse-Representation Classifiers.pdf
machinelearning/Volume 42/#3/01 - A Learning Generalization Bound with an Application to Sparse-Representation Classifiers.pdf
02 - On the Convergence of Temporal-Difference Learning with Linear Function Approximation.pdf
machinelearning/Volume 42/#3/02 - On the Convergence of Temporal-Difference Learning with Linear Function Approximation.pdf
03 - The Effect of Instance-Space Partition on Significance.pdf
machinelearning/Volume 42/#3/03 - The Effect of Instance-Space Partition on Significance.pdf
04 - Soft Margins for AdaBoost.pdf
machinelearning/Volume 42/#3/04 - Soft Margins for AdaBoost.pdf
05 - Errata.pdf
machinelearning/Volume 42/#3/05 - Errata.pdf
Volume 43
12 files • 1.68 MB
#1-2
6 files • 982.13 KB
00 - Guest Editorial.pdf
machinelearning/Volume 43/#1-2/00 - Guest Editorial.pdf
01 - Relational Reinforcement Learning.pdf
machinelearning/Volume 43/#1-2/01 - Relational Reinforcement Learning.pdf
02 - Relational Instance-Based Learning with Lists and Terms.pdf
machinelearning/Volume 43/#1-2/02 - Relational Instance-Based Learning with Lists and Terms.pdf
03 - The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures.pdf
machinelearning/Volume 43/#1-2/03 - The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures.pdf
04 - Relational Learning with Statistical Predicate Invention Better Models for Hypertext.pdf
machinelearning/Volume 43/#1-2/04 - Relational Learning with Statistical Predicate Invention Better Models for Hypertext.pdf
05 - Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming.pdf
machinelearning/Volume 43/#1-2/05 - Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming.pdf
#3
6 files • 735.2 KB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 43/#3/00 - Guest Editor's Introduction.pdf
01 - General Convergence Results for Linear Discriminant Updates.pdf
machinelearning/Volume 43/#3/01 - General Convergence Results for Linear Discriminant Updates.pdf
02 - Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions.pdf
machinelearning/Volume 43/#3/02 - Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions.pdf
03 - Worst-Case Bounds for the Logarithmic Loss of Predictors.pdf
machinelearning/Volume 43/#3/03 - Worst-Case Bounds for the Logarithmic Loss of Predictors.pdf
04 - Drifting Games.pdf
machinelearning/Volume 43/#3/04 - Drifting Games.pdf
05 - An Adaptive Version of the Boost by Majority Algorithm.pdf
machinelearning/Volume 43/#3/05 - An Adaptive Version of the Boost by Majority Algorithm.pdf
Volume 44
14 files • 1.74 MB
#1-2
9 files • 1.08 MB
00 - Introduction.pdf
machinelearning/Volume 44/#1-2/00 - Introduction.pdf
01 - Learning DFA from Simple Examples.pdf
machinelearning/Volume 44/#1-2/01 - Learning DFA from Simple Examples.pdf
02 - Learning Regular Languages from Simple Positive Examples.pdf
machinelearning/Volume 44/#1-2/02 - Learning Regular Languages from Simple Positive Examples.pdf
03 - Stochastic Finite Learning of the Pattern Languages.pdf
machinelearning/Volume 44/#1-2/03 - Stochastic Finite Learning of the Pattern Languages.pdf
04 - Efficient Algorithms for the Inference of Minimum Size DFAs.pdf
machinelearning/Volume 44/#1-2/04 - Efficient Algorithms for the Inference of Minimum Size DFAs.pdf
05 - Some Statistical-Estimation Methods for Stochastic Finite-State Transducers.pdf
machinelearning/Volume 44/#1-2/05 - Some Statistical-Estimation Methods for Stochastic Finite-State Transducers.pdf
06 - Language Simplification through Error-Correcting and Grammatical Inference Techniques.pdf
machinelearning/Volume 44/#1-2/06 - Language Simplification through Error-Correcting and Grammatical Inference Techniques.pdf
07 - Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference.pdf
machinelearning/Volume 44/#1-2/07 - Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference.pdf
08 - Stochastic Inference of Regular Tree Languages.pdf
machinelearning/Volume 44/#1-2/08 - Stochastic Inference of Regular Tree Languages.pdf
#3
5 files • 676.79 KB
00 - Editorial Inductive Logic Programming is Coming of Age.pdf
machinelearning/Volume 44/#3/00 - Editorial Inductive Logic Programming is Coming of Age.pdf
01 - On Exact Learning of Unordered Tree Patterns.pdf
machinelearning/Volume 44/#3/01 - On Exact Learning of Unordered Tree Patterns.pdf
02 - Parameter Estimation in Stochastic Logic Programs.pdf
machinelearning/Volume 44/#3/02 - Parameter Estimation in Stochastic Logic Programs.pdf
03 - Approximate Match of Rules Using Backpropagation Neural Networks.pdf
machinelearning/Volume 44/#3/03 - Approximate Match of Rules Using Backpropagation Neural Networks.pdf
04 - Extracting Context-Sensitive Models in Inductive Logic Programming.pdf
machinelearning/Volume 44/#3/04 - Extracting Context-Sensitive Models in Inductive Logic Programming.pdf
Volume 45
14 files • 2.91 MB
#1
5 files • 1.02 MB
00 - Random Forests.pdf
machinelearning/Volume 45/#1/00 - Random Forests.pdf
01 - On the VC Dimension of Bounded Margin Classifiers.pdf
machinelearning/Volume 45/#1/01 - On the VC Dimension of Bounded Margin Classifiers.pdf
02 - Iterated Phantom Induction A Knowledge-Based Approach to Learning Control.pdf
machinelearning/Volume 45/#1/02 - Iterated Phantom Induction A Knowledge-Based Approach to Learning Control.pdf
03 - Optimizing Epochal Evolutionary Search Population-Size Dependent Theory.pdf
machinelearning/Volume 45/#1/03 - Optimizing Epochal Evolutionary Search Population-Size Dependent Theory.pdf
04 - Optimizing Epochal Evolutionary Search Population-Size Dependent Theory.pdf
machinelearning/Volume 45/#1/04 - Optimizing Epochal Evolutionary Search Population-Size Dependent Theory.pdf
#2
5 files • 1.16 MB
00 - Learning with Maximum-Entropy Distributions.pdf
machinelearning/Volume 45/#2/00 - Learning with Maximum-Entropy Distributions.pdf
01 - Robust Learning with Missing Data.pdf
machinelearning/Volume 45/#2/01 - Robust Learning with Missing Data.pdf
02 - A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.pdf
machinelearning/Volume 45/#2/02 - A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.pdf
03 - Predicting the Future of Discrete Sequences from Fractal Representations of the Past.pdf
machinelearning/Volume 45/#2/03 - Predicting the Future of Discrete Sequences from Fractal Representations of the Past.pdf
04 - Reinterpreting the Category Utility Function.pdf
machinelearning/Volume 45/#2/04 - Reinterpreting the Category Utility Function.pdf
#3
4 files • 740.31 KB
00 - Efficient Construction of Regression Trees with Range and Region Splitting.pdf
machinelearning/Volume 45/#3/00 - Efficient Construction of Regression Trees with Range and Region Splitting.pdf
01 - Using Iterated Bagging to Debias Regressions.pdf
machinelearning/Volume 45/#3/01 - Using Iterated Bagging to Debias Regressions.pdf
02 - Accelerating EM for Large Databases.pdf
machinelearning/Volume 45/#3/02 - Accelerating EM for Large Databases.pdf
03 - Relative Loss Bounds for Multidimensional Regression Problems.pdf
machinelearning/Volume 45/#3/03 - Relative Loss Bounds for Multidimensional Regression Problems.pdf
Volume 46
19 files • 4.11 MB
#1-3
19 files • 4.11 MB
00 - Editorial Kernel Methods Current Research and Future Directions.pdf
machinelearning/Volume 46/#1-3/00 - Editorial Kernel Methods Current Research and Future Directions.pdf
01 - On a Connection between Kernel PCA and Metric Multidimensional Scaling.pdf
machinelearning/Volume 46/#1-3/01 - On a Connection between Kernel PCA and Metric Multidimensional Scaling.pdf
02 - Bayesian Methods for Support Vector Machines Evidence and Predictive Class Probabilities.pdf
machinelearning/Volume 46/#1-3/02 - Bayesian Methods for Support Vector Machines Evidence and Predictive Class Probabilities.pdf
03 - Hierarchical Learning in Polynomial Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/03 - Hierarchical Learning in Polynomial Support Vector Machines.pdf
04 - A Probabilistic Framework for SVM Regression and Error Bar Estimation.pdf
machinelearning/Volume 46/#1-3/04 - A Probabilistic Framework for SVM Regression and Error Bar Estimation.pdf
05 - On the Dual Formulation of Regularized Linear Systems with Convex Risks.pdf
machinelearning/Volume 46/#1-3/05 - On the Dual Formulation of Regularized Linear Systems with Convex Risks.pdf
06 - Choosing Multiple Parameters for Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/06 - Choosing Multiple Parameters for Support Vector Machines.pdf
07 - Training Invariant Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/07 - Training Invariant Support Vector Machines.pdf
08 - Support Vector Machines for Classification in Nonstandard Situations.pdf
machinelearning/Volume 46/#1-3/08 - Support Vector Machines for Classification in Nonstandard Situations.pdf
09 - An Analytic Center Machine.pdf
machinelearning/Volume 46/#1-3/09 - An Analytic Center Machine.pdf
10 - Linear Programming Boosting via Column Generation.pdf
machinelearning/Volume 46/#1-3/10 - Linear Programming Boosting via Column Generation.pdf
11 - Large Scale Kernel Regression via Linear Programming.pdf
machinelearning/Volume 46/#1-3/11 - Large Scale Kernel Regression via Linear Programming.pdf
12 - Efficient SVM Regression Training with SMO.pdf
machinelearning/Volume 46/#1-3/12 - Efficient SVM Regression Training with SMO.pdf
13 - A Simple Decomposition Method for Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/13 - A Simple Decomposition Method for Support Vector Machines.pdf
14 - Feasible Direction Decomposition Algorithms for Training Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/14 - Feasible Direction Decomposition Algorithms for Training Support Vector Machines.pdf
15 - Convergence of a Generalized SMO Algorithm for SVM Classifier Design.pdf
machinelearning/Volume 46/#1-3/15 - Convergence of a Generalized SMO Algorithm for SVM Classifier Design.pdf
15 - The Relaxed Online Maximum Margin Algorithm.pdf
machinelearning/Volume 46/#1-3/15 - The Relaxed Online Maximum Margin Algorithm.pdf
16 - Gene Selection for Cancer Classification using Support Vector Machines.pdf
machinelearning/Volume 46/#1-3/16 - Gene Selection for Cancer Classification using Support Vector Machines.pdf
17 - Text Categorization with Support Vector Machines. How to Represent Texts in Input Space.pdf
machinelearning/Volume 46/#1-3/17 - Text Categorization with Support Vector Machines. How to Represent Texts in Input Space.pdf
Volume 47
11 files • 1.85 MB
#1
5 files • 920.73 KB
00 - Editorial.pdf
machinelearning/Volume 47/#1/00 - Editorial.pdf
01 - Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data.pdf
machinelearning/Volume 47/#1/01 - Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data.pdf
02 - A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles.pdf
machinelearning/Volume 47/#1/02 - A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles.pdf
03 - Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction.pdf
machinelearning/Volume 47/#1/03 - Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction.pdf
04 - Bayesian Clustering by Dynamics.pdf
machinelearning/Volume 47/#1/04 - Bayesian Clustering by Dynamics.pdf
#2-3
6 files • 976.76 KB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 47/#2-3/00 - Guest Editor's Introduction.pdf
01 - PAC Analogues of Perceptron and Winnow Via Boosting the Margin.pdf
machinelearning/Volume 47/#2-3/01 - PAC Analogues of Perceptron and Winnow Via Boosting the Margin.pdf
02 - Boosting Methods for Regression.pdf
machinelearning/Volume 47/#2-3/02 - Boosting Methods for Regression.pdf
03 - On the Learnability and Design of Output Codes for Multiclass Problems.pdf
machinelearning/Volume 47/#2-3/03 - On the Learnability and Design of Output Codes for Multiclass Problems.pdf
04 - Finite-time Analysis of the Multiarmed Bandit Problem.pdf
machinelearning/Volume 47/#2-3/04 - Finite-time Analysis of the Multiarmed Bandit Problem.pdf
05 - Theory Revision with Queries DNF Formulas.pdf
machinelearning/Volume 47/#2-3/05 - Theory Revision with Queries DNF Formulas.pdf
Volume 48
14 files • 4.24 MB
#1-3
14 files • 4.24 MB
00 - Guest Introduction Special Issue on New Methods for Model Selection and Model Combination.pdf
machinelearning/Volume 48/#1-3/00 - Guest Introduction Special Issue on New Methods for Model Selection and Model Combination.pdf
01 - Model Selection for Small Sample Regression.pdf
machinelearning/Volume 48/#1-3/01 - Model Selection for Small Sample Regression.pdf
02 - Theoretical and Experimental Evaluation of the Subspace Information Criterion.pdf
machinelearning/Volume 48/#1-3/02 - Theoretical and Experimental Evaluation of the Subspace Information Criterion.pdf
03 - Metric-Based Methods for Adaptive Model Selection and Regularization.pdf
machinelearning/Volume 48/#1-3/03 - Metric-Based Methods for Adaptive Model Selection and Regularization.pdf
04 - Model Selection and Error Estimation.pdf
machinelearning/Volume 48/#1-3/04 - Model Selection and Error Estimation.pdf
05 - Statistical Properties and Adaptive Tuning of Support Vector Machines.pdf
machinelearning/Volume 48/#1-3/05 - Statistical Properties and Adaptive Tuning of Support Vector Machines.pdf
06 - Structural Modelling with Sparse Kernels.pdf
machinelearning/Volume 48/#1-3/06 - Structural Modelling with Sparse Kernels.pdf
07 - Kernel Matching Pursuit.pdf
machinelearning/Volume 48/#1-3/07 - Kernel Matching Pursuit.pdf
08 - Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces.pdf
machinelearning/Volume 48/#1-3/08 - Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces.pdf
09 - On the Existence of Linear Weak Learners and Applications to Boosting.pdf
machinelearning/Volume 48/#1-3/09 - On the Existence of Linear Weak Learners and Applications to Boosting.pdf
10 - Logistic Regression, AdaBoost and Bregman Distances.pdf
machinelearning/Volume 48/#1-3/10 - Logistic Regression, AdaBoost and Bregman Distances.pdf
11 - Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates.pdf
machinelearning/Volume 48/#1-3/11 - Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates.pdf
12 - Bayesian Treed Models.pdf
machinelearning/Volume 48/#1-3/12 - Bayesian Treed Models.pdf
13 - Decision Region Connectivity Analysis A Method for Analyzing High-Dimensional Classifiers.pdf
machinelearning/Volume 48/#1-3/13 - Decision Region Connectivity Analysis A Method for Analyzing High-Dimensional Classifiers.pdf
Volume 49
15 files • 3.58 MB
#1
3 files • 501.82 KB
00 - The Lagging Anchor Algorithm Reinforcement Learning in Two-Player Zero-Sum Games with Imperfect Information.pdf
machinelearning/Volume 49/#1/00 - The Lagging Anchor Algorithm Reinforcement Learning in Two-Player Zero-Sum Games with Imperfect Information.pdf
01 - A Simple Method for Generating Additive Clustering Models with Limited Complexity.pdf
machinelearning/Volume 49/#1/01 - A Simple Method for Generating Additive Clustering Models with Limited Complexity.pdf
02 - Feature Generation Using General Constructor Functions.pdf
machinelearning/Volume 49/#1/02 - Feature Generation Using General Constructor Functions.pdf
#2-3
12 files • 3.09 MB
00 - Introduction.pdf
machinelearning/Volume 49/#2-3/00 - Introduction.pdf
01 - Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks.pdf
machinelearning/Volume 49/#2-3/01 - Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks.pdf
02 - Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts.pdf
machinelearning/Volume 49/#2-3/02 - Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts.pdf
03 - Kernel-Based Reinforcement Learning.pdf
machinelearning/Volume 49/#2-3/03 - Kernel-Based Reinforcement Learning.pdf
04 - On Average Versus Discounted Reward Temporal-Difference Learning.pdf
machinelearning/Volume 49/#2-3/04 - On Average Versus Discounted Reward Temporal-Difference Learning.pdf
05 - A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes.pdf
machinelearning/Volume 49/#2-3/05 - A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes.pdf
06 - Near-Optimal Reinforcement Learning in Polynomial Time.pdf
machinelearning/Volume 49/#2-3/06 - Near-Optimal Reinforcement Learning in Polynomial Time.pdf
07 - Technical Update Least-Squares Temporal Difference Learning.pdf
machinelearning/Volume 49/#2-3/07 - Technical Update Least-Squares Temporal Difference Learning.pdf
08 - Continuous-Action Q-Learning.pdf
machinelearning/Volume 49/#2-3/08 - Continuous-Action Q-Learning.pdf
09 - Risk-Sensitive Reinforcement Learning.pdf
machinelearning/Volume 49/#2-3/09 - Risk-Sensitive Reinforcement Learning.pdf
10 - Variable Resolution Discretization in Optimal Control.pdf
machinelearning/Volume 49/#2-3/10 - Variable Resolution Discretization in Optimal Control.pdf
11 - Structure in the Space of Value Functions.pdf
machinelearning/Volume 49/#2-3/11 - Structure in the Space of Value Functions.pdf
Volume 50
14 files • 5.07 MB
#1-2
8 files • 3.51 MB
00 - An Introduction to MCMC for Machine Learning.pdf
machinelearning/Volume 50/#1-2/00 - An Introduction to MCMC for Machine Learning.pdf
01 - EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence.pdf
machinelearning/Volume 50/#1-2/01 - EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence.pdf
02 - Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation.pdf
machinelearning/Volume 50/#1-2/02 - Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation.pdf
03 - Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks.pdf
machinelearning/Volume 50/#1-2/03 - Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks.pdf
04 - Improving Markov Chain Monte Carlo Model Search for Data Mining.pdf
machinelearning/Volume 50/#1-2/04 - Improving Markov Chain Monte Carlo Model Search for Data Mining.pdf
05 - Classification with Bayesian MARS.pdf
machinelearning/Volume 50/#1-2/05 - Classification with Bayesian MARS.pdf
06 - Population Markov Chain Monte Carlo.pdf
machinelearning/Volume 50/#1-2/06 - Population Markov Chain Monte Carlo.pdf
07 - A Noninformative Prior for Neural Networks.pdf
machinelearning/Volume 50/#1-2/07 - A Noninformative Prior for Neural Networks.pdf
#3
6 files • 1.57 MB
00 - Introduction.pdf
machinelearning/Volume 50/#3/00 - Introduction.pdf
01 - Combining Classifiers with Meta Decision Trees.pdf
machinelearning/Volume 50/#3/01 - Combining Classifiers with Meta Decision Trees.pdf
02 - Ranking Learning Algorithms Using IBL and Meta-Learning on Accuracy and Time Results.pdf
machinelearning/Volume 50/#3/02 - Ranking Learning Algorithms Using IBL and Meta-Learning on Accuracy and Time Results.pdf
03 - Learning to Match the Schemas of Data Sources A Multistrategy Approach.pdf
machinelearning/Volume 50/#3/03 - Learning to Match the Schemas of Data Sources A Multistrategy Approach.pdf
04 - Clustered Partial Linear Regression.pdf
machinelearning/Volume 50/#3/04 - Clustered Partial Linear Regression.pdf
05 - Complete Mining of Frequent Patterns from Graphs Mining Graph Data.pdf
machinelearning/Volume 50/#3/05 - Complete Mining of Frequent Patterns from Graphs Mining Graph Data.pdf
Volume 51
13 files • 1.8 MB
#1
4 files • 588.74 KB
00 - PAC-Bayesian Stochastic Model Selection.pdf
machinelearning/Volume 51/#1/00 - PAC-Bayesian Stochastic Model Selection.pdf
01 - Relative Loss Bounds for Temporal-Difference Learning.pdf
machinelearning/Volume 51/#1/01 - Relative Loss Bounds for Temporal-Difference Learning.pdf
02 - Polynomial-Time Decomposition Algorithms for Support Vector Machines.pdf
machinelearning/Volume 51/#1/02 - Polynomial-Time Decomposition Algorithms for Support Vector Machines.pdf
03 - An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection.pdf
machinelearning/Volume 51/#1/03 - An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection.pdf
#2
4 files • 741.99 KB
00 - Variance and Bias for General Loss Functions.pdf
machinelearning/Volume 51/#2/00 - Variance and Bias for General Loss Functions.pdf
01 - Learning from Different Teachers.pdf
machinelearning/Volume 51/#2/01 - Learning from Different Teachers.pdf
02 - Microchoice Bounds and Self Bounding Learning Algorithms.pdf
machinelearning/Volume 51/#2/02 - Microchoice Bounds and Self Bounding Learning Algorithms.pdf
03 - Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy.pdf
machinelearning/Volume 51/#2/03 - Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy.pdf
#3
5 files • 513.57 KB
00 - Guest Editor's Introduction.pdf
machinelearning/Volume 51/#3/00 - Guest Editor's Introduction.pdf
01 - Boosting and Hard-Core Set Construction.pdf
machinelearning/Volume 51/#3/01 - Boosting and Hard-Core Set Construction.pdf
02 - Potential-Based Algorithms in On-Line Prediction and Game Theory.pdf
machinelearning/Volume 51/#3/02 - Potential-Based Algorithms in On-Line Prediction and Game Theory.pdf
03 - Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces.pdf
machinelearning/Volume 51/#3/03 - Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces.pdf
04 - A Theoretical Analysis of Query Selection for Collaborative Filtering.pdf
machinelearning/Volume 51/#3/04 - A Theoretical Analysis of Query Selection for Collaborative Filtering.pdf
Volume 52
12 files • 3.49 MB
#1-2
9 files • 2.83 MB
00 - Editorial Methods in Functional Genomics.pdf
machinelearning/Volume 52/#1-2/00 - Editorial Methods in Functional Genomics.pdf
01 - Relation Between Permutation-Test P Values and Classifier Error Estimates.pdf
machinelearning/Volume 52/#1-2/01 - Relation Between Permutation-Test P Values and Classifier Error Estimates.pdf
02 - Boosting and Microarray Data.pdf
machinelearning/Volume 52/#1-2/02 - Boosting and Microarray Data.pdf
03 - Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps.pdf
machinelearning/Volume 52/#1-2/03 - Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps.pdf
04 - Self-Organizing Latent Lattice Models for Temporal Gene Expression Profiling.pdf
machinelearning/Volume 52/#1-2/04 - Self-Organizing Latent Lattice Models for Temporal Gene Expression Profiling.pdf
05 - Consensus Clustering A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data.pdf
machinelearning/Volume 52/#1-2/05 - Consensus Clustering A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data.pdf
06 - Inclusion of Textual Documentation in the Analysis of Multidimensional Data Sets Application to Gene Expression Data.pdf
machinelearning/Volume 52/#1-2/06 - Inclusion of Textual Documentation in the Analysis of Multidimensional Data Sets Application to Gene Expression Data.pdf
07 - On Learning Gene Regulatory Networks Under the Boolean Network Model.pdf
machinelearning/Volume 52/#1-2/07 - On Learning Gene Regulatory Networks Under the Boolean Network Model.pdf
08 - External Control in Markovian Genetic Regulatory Networks.pdf
machinelearning/Volume 52/#1-2/08 - External Control in Markovian Genetic Regulatory Networks.pdf
#3
3 files • 680.44 KB
00 - Tree Induction for Probability-Based Ranking.pdf
machinelearning/Volume 52/#3/00 - Tree Induction for Probability-Based Ranking.pdf
01 - Feature Weighting in k-Means Clustering.pdf
machinelearning/Volume 52/#3/01 - Feature Weighting in k-Means Clustering.pdf
02 - Inference for the Generalization Error.pdf
machinelearning/Volume 52/#3/02 - Inference for the Generalization Error.pdf
Volume 53
8 files • 3.12 MB
#1-2
5 files • 2.49 MB
00 - A Microchoice Bound for Continuous-Space Classification Algorithms.pdf
machinelearning/Volume 53/#1-2/00 - A Microchoice Bound for Continuous-Space Classification Algorithms.pdf
01 - Theoretical and Empirical Analysis of ReliefF and RReliefF.pdf
machinelearning/Volume 53/#1-2/01 - Theoretical and Empirical Analysis of ReliefF and RReliefF.pdf
02 - Online Ensemble Learning An Empirical Study.pdf
machinelearning/Volume 53/#1-2/02 - Online Ensemble Learning An Empirical Study.pdf
03 - Programming by Demonstration Using Version Space Algebra.pdf
machinelearning/Volume 53/#1-2/03 - Programming by Demonstration Using Version Space Algebra.pdf
04 - Improved Rooftop Detection in Aerial Images with Machine Learning.pdf
machinelearning/Volume 53/#1-2/04 - Improved Rooftop Detection in Aerial Images with Machine Learning.pdf
#3
3 files • 649.92 KB
00 - Learning from Cluster Examples.pdf
machinelearning/Volume 53/#3/00 - Learning from Cluster Examples.pdf
01 - Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers.pdf
machinelearning/Volume 53/#3/01 - Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers.pdf
02 - The Robustness of the p-Norm Algorithms.pdf
machinelearning/Volume 53/#3/02 - The Robustness of the p-Norm Algorithms.pdf
Volume 54
12 files • 2.3 MB
#1
4 files • 891.72 KB
00 - Benchmarking Least Squares Support Vector Machine Classifiers.pdf
machinelearning/Volume 54/#1/00 - Benchmarking Least Squares Support Vector Machine Classifiers.pdf
01 - Projection Support Vector Machine Generators.pdf
machinelearning/Volume 54/#1/01 - Projection Support Vector Machine Generators.pdf
02 - Support Vector Data Description.pdf
machinelearning/Volume 54/#1/02 - Support Vector Data Description.pdf
03 - A Hybrid Classification Model.pdf
machinelearning/Volume 54/#1/03 - A Hybrid Classification Model.pdf
#2
3 files • 481.84 KB
00 - DeEPs A New Instance-Based Lazy Discovery and Classification System.pdf
machinelearning/Volume 54/#2/00 - DeEPs A New Instance-Based Lazy Discovery and Classification System.pdf
01 - Selective Sampling for Nearest Neighbor Classifiers.pdf
machinelearning/Volume 54/#2/01 - Selective Sampling for Nearest Neighbor Classifiers.pdf
02 - Active Sampling for Class Probability Estimation and Ranking.pdf
machinelearning/Volume 54/#2/02 - Active Sampling for Class Probability Estimation and Ranking.pdf
#3
5 files • 986.2 KB
00 - Introduction to the Special Issue on Meta-Learning.pdf
machinelearning/Volume 54/#3/00 - Introduction to the Special Issue on Meta-Learning.pdf
01 - A Meta-Learning Method to Select the Kernel Width in Support Vector Regression.pdf
machinelearning/Volume 54/#3/01 - A Meta-Learning Method to Select the Kernel Width in Support Vector Regression.pdf
02 - Optimal Ordered Problem Solver.pdf
machinelearning/Volume 54/#3/02 - Optimal Ordered Problem Solver.pdf
03 - Is Combining Classifiers with Stacking Better than Selecting the Best One.pdf
machinelearning/Volume 54/#3/03 - Is Combining Classifiers with Stacking Better than Selecting the Best One.pdf
04 - On Data and Algorithms Understanding Inductive Performance.pdf
machinelearning/Volume 54/#3/04 - On Data and Algorithms Understanding Inductive Performance.pdf
Volume 55
12 files • 2.12 MB
#1
4 files • 508.55 KB
00 - A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward.pdf
machinelearning/Volume 55/#1/00 - A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward.pdf
01 - Classification Using F-Machines and Constructive Function Approximation.pdf
machinelearning/Volume 55/#1/01 - Classification Using F-Machines and Constructive Function Approximation.pdf
02 - Khiops A Statistical Discretization Method of Continuous Attributes.pdf
machinelearning/Volume 55/#1/02 - Khiops A Statistical Discretization Method of Continuous Attributes.pdf
03 - Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers.pdf
machinelearning/Volume 55/#1/03 - Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers.pdf
#2
4 files • 794.5 KB
00 - Guest Editors’ Introduction.pdf
machinelearning/Volume 55/#2/00 - Guest Editors’ Introduction.pdf
01 - Induction as Consequence Finding.pdf
machinelearning/Volume 55/#2/01 - Induction as Consequence Finding.pdf
02 - Fast Theta-Subsumption with Constraint Satisfaction Algorithms.pdf
machinelearning/Volume 55/#2/02 - Fast Theta-Subsumption with Constraint Satisfaction Algorithms.pdf
03 - Inducing Multi-Level Association Rules from Multiple Relations.pdf
machinelearning/Volume 55/#2/03 - Inducing Multi-Level Association Rules from Multiple Relations.pdf
#3
4 files • 872.1 KB
00 - Functional Trees.pdf
machinelearning/Volume 55/#3/00 - Functional Trees.pdf
01 - Bagging Equalizes Influence.pdf
machinelearning/Volume 55/#3/01 - Bagging Equalizes Influence.pdf
02 - How to Better Use Expert Advice.pdf
machinelearning/Volume 55/#3/02 - How to Better Use Expert Advice.pdf
03 - Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering.pdf
machinelearning/Volume 55/#3/03 - Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering.pdf
Volume 56
9 files • 1.55 MB
#1-3
9 files • 1.55 MB
00 - Introduction Special Issue on Theoretical Advances in Data Clustering.pdf
machinelearning/Volume 56/#1-3/00 - Introduction Special Issue on Theoretical Advances in Data Clustering.pdf
01 - Clustering Large Graphs via the Singular Value Decomposition.pdf
machinelearning/Volume 56/#1-3/01 - Clustering Large Graphs via the Singular Value Decomposition.pdf
02 - Optimal Time Bounds for Approximate Clustering.pdf
machinelearning/Volume 56/#1-3/02 - Optimal Time Bounds for Approximate Clustering.pdf
03 - A k-Median Algorithm with Running Time Independent of Data Size.pdf
machinelearning/Volume 56/#1-3/03 - A k-Median Algorithm with Running Time Independent of Data Size.pdf
04 - Correlation Clustering.pdf
machinelearning/Volume 56/#1-3/04 - Correlation Clustering.pdf
05 - A New Conceptual Clustering Framework.pdf
machinelearning/Volume 56/#1-3/05 - A New Conceptual Clustering Framework.pdf
06 - Subquadratic Approximation Algorithms for Clustering Problems in High Dimensional Spaces.pdf
machinelearning/Volume 56/#1-3/06 - Subquadratic Approximation Algorithms for Clustering Problems in High Dimensional Spaces.pdf
07 - Central Clustering of Attributed Graphs.pdf
machinelearning/Volume 56/#1-3/07 - Central Clustering of Attributed Graphs.pdf
08 - Semi-Supervised Learning on Riemannian Manifolds.pdf
machinelearning/Volume 56/#1-3/08 - Semi-Supervised Learning on Riemannian Manifolds.pdf
Volume 57
13 files • 4.05 MB
#1-2
8 files • 3.05 MB
00 - Decision Support Through Subgroup Discovery Three Case Studies and the Lessons Learned.pdf
machinelearning/Volume 57/#1-2/00 - Decision Support Through Subgroup Discovery Three Case Studies and the Lessons Learned.pdf
01 - Editorial Data Mining Lessons Learned.pdf
machinelearning/Volume 57/#1-2/01 - Editorial Data Mining Lessons Learned.pdf
02 - Introduction Lessons Learned from Data Mining Applications and Collaborative Problem Solving.pdf
machinelearning/Volume 57/#1-2/02 - Introduction Lessons Learned from Data Mining Applications and Collaborative Problem Solving.pdf
03 - Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation.pdf
machinelearning/Volume 57/#1-2/03 - Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation.pdf
04 - Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics.pdf
machinelearning/Volume 57/#1-2/04 - Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics.pdf
05 - Lessons and Challenges from Mining Retail E-Commerce Data.pdf
machinelearning/Volume 57/#1-2/05 - Lessons and Challenges from Mining Retail E-Commerce Data.pdf
06 - Learning to Decode Cognitive States from Brain Images.pdf
machinelearning/Volume 57/#1-2/06 - Learning to Decode Cognitive States from Brain Images.pdf
07 - A Bias-Variance Analysis of a Real World Learning Problem The CoIL Challenge 2000.pdf
machinelearning/Volume 57/#1-2/07 - A Bias-Variance Analysis of a Real World Learning Problem The CoIL Challenge 2000.pdf
#3
5 files • 1.01 MB
00 - Guest Editorial.pdf
machinelearning/Volume 57/#3/00 - Guest Editorial.pdf
01 - Kernels and Distances for Structured Data.pdf
machinelearning/Volume 57/#3/01 - Kernels and Distances for Structured Data.pdf
02 - Naive Bayesian Classification of Structured Data.pdf
machinelearning/Volume 57/#3/02 - Naive Bayesian Classification of Structured Data.pdf
03 - Integrating Guidance into Relational Reinforcement Learning.pdf
machinelearning/Volume 57/#3/03 - Integrating Guidance into Relational Reinforcement Learning.pdf
04 - Compact Representation of Knowledge Bases in Inductive Logic Programming.pdf
machinelearning/Volume 57/#3/04 - Compact Representation of Knowledge Bases in Inductive Logic Programming.pdf
Volume 58
14 files • 11.95 MB
#1
5 files • 596.52 KB
00 - Not So Naive Bayes Aggregating One-Dependence Estimators.pdf
machinelearning/Volume 58/#1/00 - Not So Naive Bayes Aggregating One-Dependence Estimators.pdf
01 - On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.pdf
machinelearning/Volume 58/#1/01 - On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.pdf
02 - A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.pdf
machinelearning/Volume 58/#1/02 - A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.pdf
03 - ROC ‘n’ Rule Learning—Towards a Better Understanding of Covering Algorithms.pdf
machinelearning/Volume 58/#1/03 - ROC ‘n’ Rule Learning—Towards a Better Understanding of Covering Algorithms.pdf
04 - Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap.pdf
machinelearning/Volume 58/#1/04 - Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap.pdf
#2-3
9 files • 11.36 MB
00 - Guest Editorial.pdf
machinelearning/Volume 58/#2-3/00 - Guest Editorial.pdf
01 - Evolutionary Rule Mining in Time Series Databases.pdf
machinelearning/Volume 58/#2-3/01 - Evolutionary Rule Mining in Time Series Databases.pdf
02 - Automatic Feature Extraction for Classifying Audio Data.pdf
machinelearning/Volume 58/#2-3/02 - Automatic Feature Extraction for Classifying Audio Data.pdf
03 - Clustering Time Series with Clipped Data.pdf
machinelearning/Volume 58/#2-3/03 - Clustering Time Series with Clipped Data.pdf
04 - Classification of Multivariate Time Series and Structured Data Using Constructive Induction.pdf
machinelearning/Volume 58/#2-3/04 - Classification of Multivariate Time Series and Structured Data Using Constructive Induction.pdf
05 - Principle Components and Importance Ranking of Distributed Anomalies.pdf
machinelearning/Volume 58/#2-3/05 - Principle Components and Importance Ranking of Distributed Anomalies.pdf
06 - Fast and Exact Warping of Time Series Using Adaptive Segmental Approximations.pdf
machinelearning/Volume 58/#2-3/06 - Fast and Exact Warping of Time Series Using Adaptive Segmental Approximations.pdf
07 - Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle.pdf
machinelearning/Volume 58/#2-3/07 - Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle.pdf
08 - Elastic Translation Invariant Matching of Trajectories.pdf
machinelearning/Volume 58/#2-3/08 - Elastic Translation Invariant Matching of Trajectories.pdf
Volume 59
13 files • 2.94 MB
#1-2
7 files • 1.76 MB
00 - Evolving Soccer Keepaway Players Through Task Decomposition.pdf
machinelearning/Volume 59/#1-2/00 - Evolving Soccer Keepaway Players Through Task Decomposition.pdf
01 - A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game.pdf
machinelearning/Volume 59/#1-2/01 - A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game.pdf
02 - PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification.pdf
machinelearning/Volume 59/#1-2/02 - PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification.pdf
03 - Multicategory Proximal Support Vector Machine Classifiers.pdf
machinelearning/Volume 59/#1-2/03 - Multicategory Proximal Support Vector Machine Classifiers.pdf
04 - Maximizing Agreements with One-Sided Error with Applications to Heuristic Learning.pdf
machinelearning/Volume 59/#1-2/04 - Maximizing Agreements with One-Sided Error with Applications to Heuristic Learning.pdf
05 - Internal Regret in On-Line Portfolio Selection.pdf
machinelearning/Volume 59/#1-2/05 - Internal Regret in On-Line Portfolio Selection.pdf
06 - Logistic Model Trees.pdf
machinelearning/Volume 59/#1-2/06 - Logistic Model Trees.pdf
#3
6 files • 1.18 MB
00 - Editorial.pdf
machinelearning/Volume 59/#3/00 - Editorial.pdf
01 - Learning Bayesian Network Classifiers Searching in a Space of Partially Directed Acyclic Graphs.pdf
machinelearning/Volume 59/#3/01 - Learning Bayesian Network Classifiers Searching in a Space of Partially Directed Acyclic Graphs.pdf
02 - Latent Classification Models.pdf
machinelearning/Volume 59/#3/02 - Latent Classification Models.pdf
03 - On Discriminative Bayesian Network Classifiers and Logistic Regression.pdf
machinelearning/Volume 59/#3/03 - On Discriminative Bayesian Network Classifiers and Logistic Regression.pdf
04 - Structural Extension to Logistic Regression Discriminative Parameter Learning of Belief Net Classifiers.pdf
machinelearning/Volume 59/#3/04 - Structural Extension to Logistic Regression Discriminative Parameter Learning of Belief Net Classifiers.pdf
05 - TAN Classifiers Based on Decomposable Distributions.pdf
machinelearning/Volume 59/#3/05 - TAN Classifiers Based on Decomposable Distributions.pdf
Volume 60
11 files • 1.61 MB
#1-3
11 files • 1.61 MB
00 - Guest Editors Introduction Machine Learning in Speech and Language Technologies.pdf
machinelearning/Volume 60/#1-3/00 - Guest Editors Introduction Machine Learning in Speech and Language Technologies.pdf
01 - Support Vector Learning for Semantic Argument Classification.pdf
machinelearning/Volume 60/#1-3/01 - Support Vector Learning for Semantic Argument Classification.pdf
02 - Filtering-Ranking Perceptron Learning for Partial Parsing.pdf
machinelearning/Volume 60/#1-3/02 - Filtering-Ranking Perceptron Learning for Partial Parsing.pdf
03 - Ranking and Reranking with Perceptron.pdf
machinelearning/Volume 60/#1-3/03 - Ranking and Reranking with Perceptron.pdf
04 - Online Multiclass Learning with k-Way Limited Feedback and an Application to Utterance Classification.pdf
machinelearning/Volume 60/#1-3/04 - Online Multiclass Learning with k-Way Limited Feedback and an Application to Utterance Classification.pdf
05 - Moment Kernels for Regular Distributions.pdf
machinelearning/Volume 60/#1-3/05 - Moment Kernels for Regular Distributions.pdf
06 - Maximum Entropy Modeling A Suitable Framework to Learn Context-Dependent Lexicon Models for Statistical Machine Translation.pdf
machinelearning/Volume 60/#1-3/06 - Maximum Entropy Modeling A Suitable Framework to Learn Context-Dependent Lexicon Models for Statistical Machine Translation.pdf
07 - Maximum Entropy Models with Inequality Constraints A Case Study on Text Categorization.pdf
machinelearning/Volume 60/#1-3/07 - Maximum Entropy Models with Inequality Constraints A Case Study on Text Categorization.pdf
08 - A Neural Syntactic Language Model.pdf
machinelearning/Volume 60/#1-3/08 - A Neural Syntactic Language Model.pdf
09 - Combining Statistical Language Models via the Latent Maximum Entropy Principle.pdf
machinelearning/Volume 60/#1-3/09 - Combining Statistical Language Models via the Latent Maximum Entropy Principle.pdf
10 - Corpus-based Learning of Analogies and Semantic Relations.pdf
machinelearning/Volume 60/#1-3/10 - Corpus-based Learning of Analogies and Semantic Relations.pdf
Volume 61
7 files • 4.77 MB
#1-3
7 files • 4.77 MB
00 - Incremental Learning of Linear Model Trees.pdf
machinelearning/Volume 61/#1-3/00 - Incremental Learning of Linear Model Trees.pdf
01 - Single-Class Classification with Mapping Convergence.pdf
machinelearning/Volume 61/#1-3/01 - Single-Class Classification with Mapping Convergence.pdf
02 - The Synergy Between PAV and AdaBoost.pdf
machinelearning/Volume 61/#1-3/02 - The Synergy Between PAV and AdaBoost.pdf
03 - A High Order Cumulants Based Multivariate Nonlinear Blind Source Separation Method.pdf
machinelearning/Volume 61/#1-3/03 - A High Order Cumulants Based Multivariate Nonlinear Blind Source Separation Method.pdf
04 - Combined SVM-Based Feature Selection and Classification.pdf
machinelearning/Volume 61/#1-3/04 - Combined SVM-Based Feature Selection and Classification.pdf
05 - A Fast Dual Algorithm for Kernel Logistic Regression.pdf
machinelearning/Volume 61/#1-3/05 - A Fast Dual Algorithm for Kernel Logistic Regression.pdf
06 - Generalized Low Rank Approximations of Matrices.pdf
machinelearning/Volume 61/#1-3/06 - Generalized Low Rank Approximations of Matrices.pdf
Volume 62
9 files • 9.83 MB
#1-2
6 files • 7.77 MB
00 - Introduction to the special issue on multi-relational data mining and statistical relational learning.pdf
machinelearning/Volume 62/#1-2/00 - Introduction to the special issue on multi-relational data mining and statistical relational learning.pdf
01 - PRL A probabilistic relational language.pdf
machinelearning/Volume 62/#1-2/01 - PRL A probabilistic relational language.pdf
02 - Propositionalization-based relational subgroup discovery with RSD.pdf
machinelearning/Volume 62/#1-2/02 - Propositionalization-based relational subgroup discovery with RSD.pdf
03 - Distribution-based aggregation for relational learning with identifier attributes.pdf
machinelearning/Volume 62/#1-2/03 - Distribution-based aggregation for relational learning with identifier attributes.pdf
04 - Markov logic networks.pdf
machinelearning/Volume 62/#1-2/04 - Markov logic networks.pdf
05 - XRules An effective algorithm for structural classification of XML data.pdf
machinelearning/Volume 62/#1-2/05 - XRules An effective algorithm for structural classification of XML data.pdf
#3
3 files • 2.06 MB
00 - On Mining Summaries by Objective Measures of Interestingness.pdf
machinelearning/Volume 62/#3/00 - On Mining Summaries by Objective Measures of Interestingness.pdf
01 - A Unified View on Clustering Binary Data.pdf
machinelearning/Volume 62/#3/01 - A Unified View on Clustering Binary Data.pdf
02 - Additive Regularization Trade-Off Fusion of Training and Validation Levels in Kernel Methods.pdf
machinelearning/Volume 62/#3/02 - Additive Regularization Trade-Off Fusion of Training and Validation Levels in Kernel Methods.pdf
Volume 63
13 files • 9.23 MB
#1
3 files • 4.6 MB
00 - Extremely randomized trees.pdf
machinelearning/Volume 63/#1/00 - Extremely randomized trees.pdf
01 - Data-guided model combination by decomposition and aggregation.pdf
machinelearning/Volume 63/#1/01 - Data-guided model combination by decomposition and aggregation.pdf
02 - Model-based transductive learning of the kernel matrix.pdf
machinelearning/Volume 63/#1/02 - Model-based transductive learning of the kernel matrix.pdf
#2
5 files • 1.37 MB
00 - Asymptotic analysis of temporal-difference learning algorithms with constant step-sizes.pdf
machinelearning/Volume 63/#2/00 - Asymptotic analysis of temporal-difference learning algorithms with constant step-sizes.pdf
01 - Classification using Hierarchical Naïve Bayes models.pdf
machinelearning/Volume 63/#2/01 - Classification using Hierarchical Naïve Bayes models.pdf
02 - An algorithmic theory of learning Robust concepts and random projection.pdf
machinelearning/Volume 63/#2/02 - An algorithmic theory of learning Robust concepts and random projection.pdf
03 - Classification-based objective functions.pdf
machinelearning/Volume 63/#2/03 - Classification-based objective functions.pdf
04 - Erratum.pdf
machinelearning/Volume 63/#2/04 - Erratum.pdf
#3
5 files • 3.26 MB
00 - Machine learning and games.pdf
machinelearning/Volume 63/#3/00 - Machine learning and games.pdf
01 - Adaptive game AI with dynamic scripting.pdf
machinelearning/Volume 63/#3/01 - Adaptive game AI with dynamic scripting.pdf
02 - Universal parameter optimisation in games based on SPSA.pdf
machinelearning/Volume 63/#3/02 - Universal parameter optimisation in games based on SPSA.pdf
03 - Learning to bid in bridge.pdf
machinelearning/Volume 63/#3/03 - Learning to bid in bridge.pdf
04 - Learning long-term chess strategies from databases.pdf
machinelearning/Volume 63/#3/04 - Learning long-term chess strategies from databases.pdf
Volume 64
12 files • 3.66 MB
#1-3
12 files • 3.66 MB
00 - Foreword.pdf
machinelearning/Volume 64/#1-3/00 - Foreword.pdf
01 - Relational IBL in classical music.pdf
machinelearning/Volume 64/#1-3/01 - Relational IBL in classical music.pdf
02 - Mathematical applications of inductive logic programming.pdf
machinelearning/Volume 64/#1-3/02 - Mathematical applications of inductive logic programming.pdf
03 - Quantitative pharmacophore models with inductive logic programming.pdf
machinelearning/Volume 64/#1-3/03 - Quantitative pharmacophore models with inductive logic programming.pdf
04 - Graph kernels and Gaussian processes for relational reinforcement learning.pdf
machinelearning/Volume 64/#1-3/04 - Graph kernels and Gaussian processes for relational reinforcement learning.pdf
05 - Complexity parameters for first order classes.pdf
machinelearning/Volume 64/#1-3/05 - Complexity parameters for first order classes.pdf
06 - Guest editorial.pdf
machinelearning/Volume 64/#1-3/06 - Guest editorial.pdf
07 - First order random forests Learning relational classifiers with complex aggregates.pdf
machinelearning/Volume 64/#1-3/07 - First order random forests Learning relational classifiers with complex aggregates.pdf
08 - Randomised restarted search in ILP.pdf
machinelearning/Volume 64/#1-3/08 - Randomised restarted search in ILP.pdf
09 - Application of abductive ILP to learning metabolic network inhibition from temporal data.pdf
machinelearning/Volume 64/#1-3/09 - Application of abductive ILP to learning metabolic network inhibition from temporal data.pdf
10 - Gleaner Creating ensembles of first-order clauses to improve recall-precision curves.pdf
machinelearning/Volume 64/#1-3/10 - Gleaner Creating ensembles of first-order clauses to improve recall-precision curves.pdf
11 - Learning goal hierarchies from structured observations and expert annotations.pdf
machinelearning/Volume 64/#1-3/11 - Learning goal hierarchies from structured observations and expert annotations.pdf
Volume 65
21 files • 7.09 MB
#1
11 files • 4.35 MB
00 - Semi-supervised model-based document clustering A comparative study.pdf
machinelearning/Volume 65/#1/00 - Semi-supervised model-based document clustering A comparative study.pdf
01 - The max-min hill-climbing Bayesian network structure learning algorithm.pdf
machinelearning/Volume 65/#1/01 - The max-min hill-climbing Bayesian network structure learning algorithm.pdf
02 - Kernels as features On kernels, margins, and low-dimensional mappings.pdf
machinelearning/Volume 65/#1/02 - Kernels as features On kernels, margins, and low-dimensional mappings.pdf
03 - Cost curves An improved method for visualizing classifier performance.pdf
machinelearning/Volume 65/#1/03 - Cost curves An improved method for visualizing classifier performance.pdf
04 - MODL A Bayes optimal discretization method for continuous attributes.pdf
machinelearning/Volume 65/#1/04 - MODL A Bayes optimal discretization method for continuous attributes.pdf
05 - Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming.pdf
machinelearning/Volume 65/#1/05 - Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming.pdf
06 - Learning decomposable markov networks in pseudo-independent domains with local evaluation.pdf
machinelearning/Volume 65/#1/06 - Learning decomposable markov networks in pseudo-independent domains with local evaluation.pdf
07 - An efficient top-down search algorithm for learning Boolean networks of gene expression.pdf
machinelearning/Volume 65/#1/07 - An efficient top-down search algorithm for learning Boolean networks of gene expression.pdf
08 - An analysis of diversity measures.pdf
machinelearning/Volume 65/#1/08 - An analysis of diversity measures.pdf
09 - Training a reciprocal-sigmoid classifier by feature scaling-space.pdf
machinelearning/Volume 65/#1/09 - Training a reciprocal-sigmoid classifier by feature scaling-space.pdf
10 - A suffix tree approach to anti-spam email filtering.pdf
machinelearning/Volume 65/#1/10 - A suffix tree approach to anti-spam email filtering.pdf
#2-3
10 files • 2.73 MB
00 - Guest editorial Machine learning in and for music.pdf
machinelearning/Volume 65/#2-3/00 - Guest editorial Machine learning in and for music.pdf
01 - Guest Editorial Machine learning in and for music.pdf
machinelearning/Volume 65/#2-3/01 - Guest Editorial Machine learning in and for music.pdf
02 - Melodic analysis with segment classes.pdf
machinelearning/Volume 65/#2-3/02 - Melodic analysis with segment classes.pdf
03 - Modeling, analyzing, and synthesizing expressive piano performance with graphical models.pdf
machinelearning/Volume 65/#2-3/03 - Modeling, analyzing, and synthesizing expressive piano performance with graphical models.pdf
04 - Aligning music audio with symbolic scores using a hybrid graphical model.pdf
machinelearning/Volume 65/#2-3/04 - Aligning music audio with symbolic scores using a hybrid graphical model.pdf
05 - A case based approach to expressivity-aware tempo transformation.pdf
machinelearning/Volume 65/#2-3/05 - A case based approach to expressivity-aware tempo transformation.pdf
06 - Classification-based melody transcription.pdf
machinelearning/Volume 65/#2-3/06 - Classification-based melody transcription.pdf
07 - Bootstrap learning for accurate onset detection.pdf
machinelearning/Volume 65/#2-3/07 - Bootstrap learning for accurate onset detection.pdf
08 - Aggregate features and ADA BOOST for music classification.pdf
machinelearning/Volume 65/#2-3/08 - Aggregate features and ADA BOOST for music classification.pdf
09 - Using duration models to reduce fragmentation in audio segmentation.pdf
machinelearning/Volume 65/#2-3/09 - Using duration models to reduce fragmentation in audio segmentation.pdf
Volume 66
15 files • 4.27 MB
#1
5 files • 1.51 MB
00 - Guest editorial to the special issue on grammatical inference.pdf
machinelearning/Volume 66/#1/00 - Guest editorial to the special issue on grammatical inference.pdf
01 - LARS A learning algorithm for rewriting systems.pdf
machinelearning/Volume 66/#1/01 - LARS A learning algorithm for rewriting systems.pdf
02 - Interactive learning of node selecting tree transducer.pdf
machinelearning/Volume 66/#1/02 - Interactive learning of node selecting tree transducer.pdf
03 - Learning finite-state models for machine translation.pdf
machinelearning/Volume 66/#1/03 - Learning finite-state models for machine translation.pdf
04 - Learning deterministic context free grammars The Omphalos competition.pdf
machinelearning/Volume 66/#1/04 - Learning deterministic context free grammars The Omphalos competition.pdf
#2-3
10 files • 2.75 MB
00 - Guest editorial Learning theory.pdf
machinelearning/Volume 66/#2-3/00 - Guest editorial Learning theory.pdf
01 - Suboptimal behavior of Bayes and MDL in classification under misspecification.pdf
machinelearning/Volume 66/#2-3/01 - Suboptimal behavior of Bayes and MDL in classification under misspecification.pdf
02 - A new PAC bound for intersection-closed concept classes.pdf
machinelearning/Volume 66/#2-3/02 - A new PAC bound for intersection-closed concept classes.pdf
03 - Model selection by bootstrap penalization for classification.pdf
machinelearning/Volume 66/#2-3/03 - Model selection by bootstrap penalization for classification.pdf
04 - Optimal dyadic decision trees.pdf
machinelearning/Volume 66/#2-3/04 - Optimal dyadic decision trees.pdf
05 - A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering.pdf
machinelearning/Volume 66/#2-3/05 - A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering.pdf
06 - Statistical properties of kernel principal component analysis.pdf
machinelearning/Volume 66/#2-3/06 - Statistical properties of kernel principal component analysis.pdf
07 - Statistical properties of kernel principal component analysis.pdf
machinelearning/Volume 66/#2-3/07 - Statistical properties of kernel principal component analysis.pdf
08 - Feature space perspectives for learning the kernel.pdf
machinelearning/Volume 66/#2-3/08 - Feature space perspectives for learning the kernel.pdf
09 - Improved second-order bounds for prediction with expert advice.pdf
machinelearning/Volume 66/#2-3/09 - Improved second-order bounds for prediction with expert advice.pdf
Volume 67
10 files • 17.67 MB
#1-2
7 files • 16.74 MB
00 - Introduction to the special issue on learning and computational game theory.pdf
machinelearning/Volume 67/#1-2/00 - Introduction to the special issue on learning and computational game theory.pdf
01 - Slow emergence of cooperation for win-stay lose-shift on trees.pdf
machinelearning/Volume 67/#1-2/01 - Slow emergence of cooperation for win-stay lose-shift on trees.pdf
02 - AWESOME A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents.pdf
machinelearning/Volume 67/#1-2/02 - AWESOME A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents.pdf
03 - A general criterion and an algorithmic framework for learning in multi-agent systems.pdf
machinelearning/Volume 67/#1-2/03 - A general criterion and an algorithmic framework for learning in multi-agent systems.pdf
04 - Online calibrated forecasts Memory efficiency versus universality for learning in games.pdf
machinelearning/Volume 67/#1-2/04 - Online calibrated forecasts Memory efficiency versus universality for learning in games.pdf
05 - Bidding agents for online auctions with hidden bids.pdf
machinelearning/Volume 67/#1-2/05 - Bidding agents for online auctions with hidden bids.pdf
06 - Learning payoff functions in infinite games.pdf
machinelearning/Volume 67/#1-2/06 - Learning payoff functions in infinite games.pdf
#3
3 files • 950.92 KB
00 - Density estimation with stagewise optimization of the empirical risk.pdf
machinelearning/Volume 67/#3/00 - Density estimation with stagewise optimization of the empirical risk.pdf
01 - Stability of Unstable Learning Algorithms.pdf
machinelearning/Volume 67/#3/01 - Stability of Unstable Learning Algorithms.pdf
02 - Multi-Class Learning by Smoothed Boosting.pdf
machinelearning/Volume 67/#3/02 - Multi-Class Learning by Smoothed Boosting.pdf
Volume 68
4 files • 2.71 MB
#1
4 files • 2.71 MB
00 - Discovering Significant Patterns.pdf
machinelearning/Volume 68/#1/00 - Discovering Significant Patterns.pdf
01 - Invariant kernel functions for pattern analysis and machine learning.pdf
machinelearning/Volume 68/#1/01 - Invariant kernel functions for pattern analysis and machine learning.pdf
02 - Predictive Modelling of Heterogeneous Sequence Collections by Topographic Ordering of Histories.pdf
machinelearning/Volume 68/#1/02 - Predictive Modelling of Heterogeneous Sequence Collections by Topographic Ordering of Histories.pdf
03 - PAV and the ROC convex hull.pdf
machinelearning/Volume 68/#1/03 - PAV and the ROC convex hull.pdf
Volume 70
4 files • 2.5 MB
#1
4 files • 2.5 MB
article1.pdf
machinelearning/Volume 70/#1/article1.pdf
article2.pdf
machinelearning/Volume 70/#1/article2.pdf
article3.pdf
machinelearning/Volume 70/#1/article3.pdf
article4.pdf
machinelearning/Volume 70/#1/article4.pdf
Volume 72
8 files • 3.63 MB
#3
8 files • 3.63 MB
article1.pdf
machinelearning/Volume 72/#3/article1.pdf
article2.pdf
machinelearning/Volume 72/#3/article2.pdf
article3.pdf
machinelearning/Volume 72/#3/article3.pdf
article4.pdf
machinelearning/Volume 72/#3/article4.pdf
article5.pdf
machinelearning/Volume 72/#3/article5.pdf
article6.pdf
machinelearning/Volume 72/#3/article6.pdf
article7.pdf
machinelearning/Volume 72/#3/article7.pdf
article8.pdf
machinelearning/Volume 72/#3/article8.pdf
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