Machine Learning Proceedings 1994

Description

Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

Additional details

Published: 1994
Imprint: Morgan Kaufmann
Language: English
ISBN: 978-1-55860-335-6
DOI: 10.1016/C2009-0-27542-8

Actions for selected chapters

Select all Front Matter

CONTRIBUTED PAPERS

Select A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars
Book chapterAbstract only
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars
Naoki Abe and Hiroshi Mamitsuka
Pages 3-11
Purchase View chapter
Select Learning Recursive Relations with Randomly Selected Small Training Sets
Book chapterAbstract only
Learning Recursive Relations with Randomly Selected Small Training Sets
David W. Aha, Stephane Lapointe, ... Stan Matwin
Pages 12-18
Purchase View chapter
Select Improving Accuracy of Incorrect Domain Theories
Book chapterAbstract only
Improving Accuracy of Incorrect Domain Theories
Lars Asker
Pages 19-27
Purchase View chapter
Select Greedy Attribute Selection
Book chapterAbstract only
Greedy Attribute Selection
Rich Caruana and Dayne Freitag
Pages 28-36
Purchase View chapter
Select Using Sampling and Queries to Extract Rules from Trained Neural Networks
Book chapterAbstract only
Using Sampling and Queries to Extract Rules from Trained Neural Networks
Mark W. Craven and Jude W. Shavlik
Pages 37-45
Purchase View chapter
Select The Generate, Test, and Explain Discovery System Architecture
Book chapterAbstract only
The Generate, Test, and Explain Discovery System Architecture
Michael de la Maza
Pages 46-52
Purchase View chapter
Select Boosting and Other Machine Learning Algorithms
Book chapterAbstract only
Boosting and Other Machine Learning Algorithms
Harris Drucker, Corinna Cortes, ... Vladimir Vapnik
Pages 53-61
Purchase View chapter
Select In Defense of C4.5: Notes on Learning One-Level Decision Trees
Book chapterAbstract only
In Defense of C4.5: Notes on Learning One-Level Decision Trees
Tapio Elomaa
Pages 62-69
Purchase View chapter
Select Incremental Reduced Error Pruning
Book chapterAbstract only
Incremental Reduced Error Pruning
Johannes Fürnkranz and Gerhard Widmer
Pages 70-77
Purchase View chapter
Select An Incremental Learning Approach for Completable Planning
Book chapterAbstract only
An Incremental Learning Approach for Completable Planning
Melinda T. Gervasio and Gerald F. DeJong
Pages 78-86
Purchase View chapter
Select Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains
Book chapterAbstract only
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains
Yolanda Gil
Pages 87-95
Purchase View chapter
Select Learning Disjunctive Concepts by Means of Genetic Algorithms
Book chapterAbstract only
Learning Disjunctive Concepts by Means of Genetic Algorithms
Attilio Giordana, Lorenza Saitta and Floriano Zini
Pages 96-104
Purchase View chapter
Select Consideration of Risk in Reinforcement Learning
Book chapterAbstract only
Consideration of Risk in Reinforcement Learning
Matthias Heger
Pages 105-111
Purchase View chapter
Select Rule Induction for Semantic Query Optimization
Book chapterAbstract only
Rule Induction for Semantic Query Optimization
Chun-Nan Hsu and Craig A. Knoblock
Pages 112-120
Purchase View chapter
Select Irrelevant Features and the Subset Selection Problem
Book chapterAbstract only
Irrelevant Features and the Subset Selection Problem
George H. John, Ron Kohavi and Karl Pfleger
Pages 121-129
Purchase View chapter
Select An Efficient Subsumption Algorithm for Inductive Logic Programming
Book chapterAbstract only
An Efficient Subsumption Algorithm for Inductive Logic Programming
Jörg-Uwe Kietz and Marcus Lübbe
Pages 130-138
Purchase View chapter
Select Getting the Most from Flawed Theories
Book chapterAbstract only
Getting the Most from Flawed Theories
Moshe Koppel, Alberto Maria Segre and Ronen Feldman
Pages 139-147
Purchase View chapter
Select Heterogeneous Uncertainty Sampling for Supervised Learning
Book chapterAbstract only
Heterogeneous Uncertainty Sampling for Supervised Learning
David D. Lewis and Jason Catlett
Pages 148-156
Purchase View chapter
Select Markov games as a framework for multi-agent reinforcement learning
Book chapterAbstract only
Markov games as a framework for multi-agent reinforcement learning
Michael L. Littman
Pages 157-163
Purchase View chapter
Select To Discount or not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning
Book chapterAbstract only
To Discount or not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning
Sridhar Mahadevan
Pages 164-172
Purchase View chapter
Select Comparing Methods for Refining Certainty-Factor Rule-Bases
Book chapterAbstract only
Comparing Methods for Refining Certainty-Factor Rule-Bases
J. Jeffrey Mahoney and Raymond J. Mooney
Pages 173-180
Purchase View chapter
Select Reward Functions for Accelerated Learning
Book chapterAbstract only
Reward Functions for Accelerated Learning
Maja J Mataric
Pages 181-189
Purchase View chapter
Select Efficient Algorithms for Minimizing Cross Validation Error
Book chapterAbstract only
Efficient Algorithms for Minimizing Cross Validation Error
Andrew W. Moore and Mary S. Lee
Pages 190-198
Purchase View chapter
Select Revision of Production System Rule-Bases
Book chapterAbstract only
Revision of Production System Rule-Bases
Patrick M. Murphy and Michael J. Pazzani
Pages 199-207
Purchase View chapter
Select Using Genetic Search to Refine Knowledge-Based Neural Networks
Book chapterAbstract only
Using Genetic Search to Refine Knowledge-Based Neural Networks
David W. Opitz and Jude W. Shavlik
Pages 208-216
Purchase View chapter
Select Reducing Misclassification Costs
Book chapterAbstract only
Reducing Misclassification Costs
Michael Pazzani, Christopher Merz, ... Clifford Brunk
Pages 217-225
Purchase View chapter
Select Incremental Multi-Step Q-Learning
Book chapterAbstract only
Incremental Multi-Step Q-Learning
Jing Peng and Ronald J. Williams
Pages 226-232
Purchase View chapter
Select The Minimum Description Length Principle and Categorical Theories
Book chapterAbstract only
The Minimum Description Length Principle and Categorical Theories
J.R. Quinlan
Pages 233-241
Purchase View chapter
Select Towards a Better Understanding of Memory-Based Reasoning Systems
Book chapterAbstract only
Towards a Better Understanding of Memory-Based Reasoning Systems
John Rachlin, Simon Kasif, ... David W. Aha
Pages 242-250
Purchase View chapter
Select Hierarchical Self-Organization in Genetic Programming
Book chapterAbstract only
Hierarchical Self-Organization in Genetic Programming
Justinian P. Rosca and Dana H. Ballard
Pages 251-258
Purchase View chapter
Select A Conservation Law for Generalization Performance
Book chapterAbstract only
A Conservation Law for Generalization Performance
Cullen Schaffer
Pages 259-265
Purchase View chapter
Select On the Worst-case Analysis of Temporal-difference Learning Algorithms
Book chapterAbstract only
On the Worst-case Analysis of Temporal-difference Learning Algorithms
Robert E. Schapire and Manfred K. Warmuth
Pages 266-274
Purchase View chapter
Select A Constraint-Based Induction Algorithm in FOL
Book chapterAbstract only
A Constraint-Based Induction Algorithm in FOL
Michèle Sebag
Pages 275-283
Purchase View chapter
Select Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Book chapterAbstract only
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Satinder P. Singh, Tommi Jaakkola and Michael I. Jordan
Pages 284-292
Purchase View chapter
Select Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
Book chapterAbstract only
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
David B. Skalak
Pages 293-301
Purchase View chapter
Select A Bayesian Framework to Integrate Symbolic and Neural Learning
Book chapterAbstract only
A Bayesian Framework to Integrate Symbolic and Neural Learning
Irina Tchoumatchenko and Jean-Gabriel Ganascia
Pages 302-308
Purchase View chapter
Select A Modular Q-Learning Architecture for Manipulator Task Decomposition
Book chapterAbstract only
A Modular Q-Learning Architecture for Manipulator Task Decomposition
Chen K. Tham and Richard W. Prager
Pages 309-317
Purchase View chapter
Select An Improved Algorithm for Incremental Induction of Decision Trees
Book chapterAbstract only
An Improved Algorithm for Incremental Induction of Decision Trees
Paul E. Utgoff
Pages 318-325
Purchase View chapter
Select A Powerful Heuristic for the Discovery of Complex Patterned Behavior
Book chapterAbstract only
A Powerful Heuristic for the Discovery of Complex Patterned Behavior
Raúl E. Valdés-Pérez and Aurora Pérez
Pages 326-334
Purchase View chapter
Select Small Sample Decision Tree Pruning
Book chapterAbstract only
Small Sample Decision Tree Pruning
Sholom M. Weiss and Nitin Indurkhya
Pages 335-342
Purchase View chapter
Select Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
Book chapterAbstract only
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser
Pages 343-351
Purchase View chapter
Select Selective Reformulation of Examples in Concept Learning
Book chapterAbstract only
Selective Reformulation of Examples in Concept Learning
Jean-Daniel Zucker and Jean-Gabriel Ganascia
Pages 352-360
Purchase View chapter

INVITED TALKS

Select A Statistical Approach to Decision Tree Modeling
Book chapterAbstract only
A Statistical Approach to Decision Tree Modeling
Michael I. Jordan
Pages 363-370
Purchase View chapter
Select Bayesian Inductive Logic Programming
Book chapterAbstract only
Bayesian Inductive Logic Programming
Stephen Muggleton
Pages 371-379
Purchase View chapter
Select Frequencies vs Biases: Machine Learning Problems in Natural Language Processing — Abstract
Book chapterAbstract only
Frequencies vs Biases: Machine Learning Problems in Natural Language Processing — Abstract
Fernando C.N. Pereira
Page 380
Purchase View chapter

Select INDEX

Book chapter

INDEX

Page 381

Purchase View chapter

William W. Cohen

AT&T Bell Laboratories

Haym Hirsh

Rutgers University

Machine Learning Proceedings 1994

Machine Learning Proceedings 1994

About the book

Description

Additional details

Actions for selected chapters

Table of contents

CONTRIBUTED PAPERS

INVITED TALKS

Editors

William W. Cohen

Haym Hirsh

Copyright