Machine Learning Proceedings 1995

Description

Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

Additional details

Published: 1995
Imprint: Morgan Kaufmann
Language: English
ISBN: 978-1-55860-377-6
DOI: 10.1016/C2009-0-27705-1

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CONTRIBUTED PAPERS

Select On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
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On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
Naoki Abe, Hang Li and Atsuyoshi Nakamura
Pages 3-11
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Select On Handling Tree-Structured Attributes in Decision Tree Learning
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On Handling Tree-Structured Attributes in Decision Tree Learning
Hussein Almuallim, Yasuhiro Akiba and Shigeo Kaneda
Pages 12-20
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Select Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
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Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
Peter Auer, Robert C. Holte and Wolfgang Maass
Pages 21-29
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Select Residual Algorithms: Reinforcement Learning with Function Approximation
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Residual Algorithms: Reinforcement Learning with Function Approximation
Leemon Baird
Pages 30-37
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Select Removing the Genetics from the Standard Genetic Algorithm
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Removing the Genetics from the Standard Genetic Algorithm
Shumeet Baluja and Rich Caruana
Pages 38-46
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Select Inductive Learning of Reactive Action Models
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Inductive Learning of Reactive Action Models
Scott Benson
Pages 47-54
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Select Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
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Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Justine Blackmore and Risto Miikkulainen
Pages 55-63
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Select Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain
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Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain
Avrim Blum
Pages 64-72
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Select Automatic Selection of Split Criterion during Tree Growing Based on Node Location
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Automatic Selection of Split Criterion during Tree Growing Based on Node Location
Carla E. Brodley
Pages 73-80
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Select A Lexically Based Semantic Bias for Theory Revision
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A Lexically Based Semantic Bias for Theory Revision
Clifford Brunk and Michael Pazzani
Pages 81-89
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Select A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
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A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
Philip K. Chan and Salvatore J. Stolfo
Pages 90-98
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Select Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
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Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
Pawel Cichosz and Jan J. Mulawka
Pages 99-107
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Select K*: An Instance-based Learner Using an Entropic Distance Measure
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K^*: An Instance-based Learner Using an Entropic Distance Measure
John G. Cleary and Leonard E. Trigg
Pages 108-114
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Select Fast Effective Rule Induction
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Fast Effective Rule Induction
William W. Cohen
Pages 115-123
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Select Text Categorization and Relational Learning
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Text Categorization and Relational Learning
William W. Cohen
Pages 124-132
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Select Protein Folding: Symbolic Refinement Competes with Neural Networks
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Protein Folding: Symbolic Refinement Competes with Neural Networks
Susan Craw and Paul Hutton
Pages 133-141
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Select A Bayesian Analysis of Algorithms for Learning Finite Functions
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A Bayesian Analysis of Algorithms for Learning Finite Functions
James Cussens
Pages 142-149
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Select Committee-Based Sampling For Training Probabilistic Classifiers
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Committee-Based Sampling For Training Probabilistic Classifiers
Ido Dagan and Sean P. Engelson
Pages 150-157
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Select Learning Prototypical Concept Descriptions
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Learning Prototypical Concept Descriptions
Piew Datta and Dennis Kibler
Pages 158-166
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Select A Case Study of Explanation-Based Control
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A Case Study of Explanation-Based Control
Gerald DeJong
Pages 167-175
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Select Explanation-Based Learning and Reinforcement Learning: A Unified View
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Explanation-Based Learning and Reinforcement Learning: A Unified View
Thomas G. Dietterich and Nicholas S. Flann
Pages 176-184
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Select Lessons from Theory Revision Applied to Constructive Induction
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Lessons from Theory Revision Applied to Constructive Induction
Steven K. Donoho and Larry A. Rendell
Pages 185-193
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Select Supervised and Unsupervised Discretization of Continuous Features
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Supervised and Unsupervised Discretization of Continuous Features
James Dougherty, Ron Kohavi and Mehran Sahami
Pages 194-202
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Select Bounds on the Classification Error of the Nearest Neighbor Rule
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Bounds on the Classification Error of the Nearest Neighbor Rule
John A. Drakopoulos
Pages 203-208
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Select Q-Learning for Bandit Problems
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Q-Learning for Bandit Problems
Michael O. Duff
Pages 209-217
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Select Distilling Reliable Information From Unreliable Theories
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Distilling Reliable Information From Unreliable Theories
Sean P. Engelson and Moshe Koppel
Pages 218-225
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Select A Quantitative Study of Hypothesis Selection
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A Quantitative Study of Hypothesis Selection
Philip W.L. Fong
Pages 226-234
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Select Learning proof heuristics by adapting parameters
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Learning proof heuristics by adapting parameters
Matthias Fuchs
Pages 235-243
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Select Efficient Algorithms for Finding Multi-way Splits for Decision Trees
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Efficient Algorithms for Finding Multi-way Splits for Decision Trees
Truxton Fulton, Simon Kasif and Steven Salzberg
Pages 244-251
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Select Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
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Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
Luca M. Gambardella and Marco Dorigo
Pages 252-260
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Select Stable Function Approximation in Dynamic Programming
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Stable Function Approximation in Dynamic Programming
Geoffrey J. Gordon
Pages 261-268
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Select The Challenge of Revising an Impure Theory
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The Challenge of Revising an Impure Theory
Russell Greiner
Pages 269-277
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Select Symbiosis in Multimodal Concept Learning
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Symbiosis in Multimodal Concept Learning
Jukka Hekanaho
Pages 278-285
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Select Tracking the Best Expert*
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Tracking the Best Expert
Mark Herbster and Manfred Warmuth
Pages 286-294
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Select Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
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Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
Hajime Kimura, Masayuki Yamamura and Shigenobu Kobayashi
Pages 295-303
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Select Automatic Parameter Selection by Minimizing Estimated Error
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Automatic Parameter Selection by Minimizing Estimated Error
Ron Kohavi and George H. John
Pages 304-312
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Select Error-Correcting Output Coding Corrects Bias and Variance
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Error-Correcting Output Coding Corrects Bias and Variance
Eun Bae Kong and Thomas G. Dietterich
Pages 313-321
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Select Learning to Make Rent-to-Buy Decisions with Systems Applications
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Learning to Make Rent-to-Buy Decisions with Systems Applications
P. Krishnan, Philip M. Long and Jeffrey Scott Vitter
Pages 322-330
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Select NewsWeeder: Learning to Filter Netnews
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NewsWeeder: Learning to Filter Netnews
Ken Lang
Pages 331-339
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Select Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
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Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
Kevin J. Lang
Pages 340-343
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Select Case-Based Acquisition of Place Knowledge
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Case-Based Acquisition of Place Knowledge
PAT LANGLEY and KARL PFLEGER
Pages 344-352
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Select Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes
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Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes
Nick Littlestone
Pages 353-361
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Select Learning policies for partially observable environments: Scaling up
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Learning policies for partially observable environments: Scaling up
Michael L. Littman, Anthony R. Cassandra and Leslie Pack Kaelbling
Pages 362-370
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Select Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves
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Increasing the performance and consistency of classification trees by using the accuracy criterion at the leaves
David J. Lubinsky
Pages 371-377
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Select Efficient Learning with Virtual Threshold Gates
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Efficient Learning with Virtual Threshold Gates
Wolfgang Maass and Manfred K. Warmuth
Pages 378-386
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Select Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
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Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
R. Andrew McCallum
Pages 387-395
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Select Efficient Learning from Delayed Rewards through Symbiotic Evolution
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Efficient Learning from Delayed Rewards through Symbiotic Evolution
David E. Moriarty and Risto Miikkulainen
Pages 396-404
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Select Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
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Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions
Partha Niyogi
Pages 405-412
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Select On learning Decision Committees
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On learning Decision Committees
Richard Nock and Olivier Gascuel
Pages 413-420
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Select Inferring Reduced Ordered Decision Graphs of Minimum Description Length
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Inferring Reduced Ordered Decision Graphs of Minimum Description Length
Arlindo L. Oliveira and Alberto Sangiovanni-Vincentelli
Pages 421-429
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Select On Pruning and Averaging Decision Trees
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On Pruning and Averaging Decision Trees
Jonathan J. Oliver and David J. Hand
Pages 430-437
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Select Efficient Memory-Based Dynamic Programming
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Efficient Memory-Based Dynamic Programming
Jing Peng
Pages 438-446
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Select Using Multidimensional Projection to Find Relations
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Using Multidimensional Projection to Find Relations
Eduardo Pérez and Larry A. Rendell
Pages 447-455
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Select Compression-Based Discretization of Continuous Attributes
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Compression-Based Discretization of Continuous Attributes
Bernhard Pfahringer
Pages 456-463
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Select MDL and Categorical Theories (Continued)
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MDL and Categorical Theories (Continued)
J.R. Quinlan
Pages 464-470
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Select For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
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For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance
R. Bharat Rao, Diana Gordon and William Spears
Pages 471-479
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Select Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices
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Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices
Marcos Salganicoff and Lyle H. Ungar
Pages 480-487
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Select Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
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Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
Jürgen Schmidhuber
Pages 488-496
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Select A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers
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A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers
Moninder Singh and Gregory M. Provan
Pages 497-505
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Select Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
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Retrofitting Decision Tree Classifiers Using Kernel Density Estimation
Padhraic Smyth, Alexander Gray and Usama M. Fayyad
Pages 506-514
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Select Automatic Speaker Recognition: An Application of Machine Learning
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Automatic Speaker Recognition: An Application of Machine Learning
Brett Squires and Claude Sammut
Pages 515-521
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Select An Inductive Learning Approach to Prognostic Prediction
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An Inductive Learning Approach to Prognostic Prediction
W. Nick Street, O.L. Mangasarian and W.H. Wolberg
Pages 522-530
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Select TD Models: Modeling the World at a Mixture of Time Scales
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TD Models: Modeling the World at a Mixture of Time Scales
Richard S. Sutton
Pages 531-539
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Select Learning Collection Fusion Strategies for Information Retrieval
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Learning Collection Fusion Strategies for Information Retrieval
Geoffrey Towell, Ellen M. Voorhees, ... Ben Johnson-Laird
Pages 540-548
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Select Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition
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Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition
Xuemei Wang
Pages 549-557
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Select Learning with Rare Cases and Small Disjuncts
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Learning with Rare Cases and Small Disjuncts
Gary M. Weiss
Pages 558-565
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Select Horizontal Generalization
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Horizontal Generalization
David H. Wolpert
Pages 566-574
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Select Learning Hierarchies from Ambiguous Natural Language Data
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Learning Hierarchies from Ambiguous Natural Language Data
Takefumi Yamazaki, Michael J. Pazzani and Christopher Merz
Pages 575-583
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INVITED TALKS (ABSTRACTS ONLY)

Select Machine Learning and Information Retrieval
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Machine Learning and Information Retrieval
W. Bruce Croft
Page 587
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Select Learning With Bayesian Networks
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Learning With Bayesian Networks
David Heckerman
Page 588
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Select Learning for Automotive Collision Avoidance and Autonomous Control
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Learning for Automotive Collision Avoidance and Autonomous Control
Dean A. Pomerleau
Page 589
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Select Author Index

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Author Index

Page 591

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Armand Prieditis

Department of Computer Science, University of California, Davis, CA

Stuart Russell

Computer Science Division, University of California, Berkeley, CA

Machine Learning Proceedings 1995

Machine Learning Proceedings 1995

About the book

Description

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Table of contents

CONTRIBUTED PAPERS

INVITED TALKS (ABSTRACTS ONLY)

Editors

Armand Prieditis

Stuart Russell

Copyright