Proceedings of the Fourth International Workshop on Machine Learning : June 22-25, 1987, University of California, Irvine

Proceedings of the Fourth International Workshop on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition. Organized into 39 chapters, this book begins with an overview of pattern recognition systems of necessity that incorporate an approximate-matching process to determine the degree of similarity between an unknown input and all stored references. This text then describes the rationale in the Protos system for relegating inductive learning and deductive problem solving to minor roles in support of retaining, indexing and matching exemplars. Other chapters consider the power as well as the appropriateness of exemplar-based representations and their associated acquisition methods. This book discusses as well the extensions to the way a case is classified by a decision tree that address shortcomings. The final chapter deals with the advances in machine learning research. This book is a valuable resource for psychologists, scientists, theorists, and research workers. Content: Front Matter, Page i Copyright, Page ii PREFACE: The Emerging Science of Machine Learning, Pages v-vi Learning about speech sounds: The NEXUS Project, Pages 1-11, GARY BRADSHAW Protos: An Exemplar-Based Learning Apprentice, Pages 12-23, E. Ray Bareiss, Bruce W. Porter, Craig C. Wier Learning Representative Exemplars of Concepts: An Initial Case Study, Pages 24-30, DENNIS KIBLER, DAVID W. AHA DECISION TREES AS PROBABILISTIC CLASSIFIERS, Pages 31-37, J.R. Quinlan Conceptual Clustering, Learning from Examples, and Inference, Pages 38-49, DOUGLAS H. FISHER How to Learn Imprecise Concepts: A Method for Employing a Two-Tiered Knowledge Representation in Learning, Pages 50-58, RYSZARD S. MICHALSKI Quasi-Darwinian Learning in a Classifier System, Pages 59-65, STEWART W. WILSON MORE ROBUST CONCEPT LEARNING USING DYNAMICALLY – VARIABLE BIAS, Pages 66-78, LARRY RENDELL, RAJ SESHU, DAVID TCHENG Incremental Adjustment of Representations for Learning, Pages 79-90, JEFFREY C. SCHLIMMER Concept Learning in Context, Pages 91-102, RICHARD M. KELLER Strategy Learning with Multilayer Connectionist Representations, Pages 103-114, Charles W. Anderson Learning a Preference Predicate, Pages 115-121, PAUL E. UTGOFF, SHARAD SAXENA Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System, Pages 122-133, STEVEN MINTON, JAIME G. CARBONELL, OREN ETZIONI, CRAIG A. KNOBLOCK, DANIEL R. KUOKKA The Anatomy of a Weak Learning Method for Use in Goal Directed Search, Pages 134-140, T.L. McCLUSKEY Learning and Reusing Explanations, Pages 141-147, Kristian J. Hammond LT Revisited: Experimental Results of Applying Explanation-Based Learning to the Logic of Principia Mathematica, Pages 148-159, PAUL O'RORKE WHAT IS AN EXPLANATION IN DISCIPLE?, Pages 160-166, YVES KODRATOFF, GHEORGHE TECUCI Extending Problem Solver Capabilities Through Case-Based Inference, Pages 167-178, JANET L. KOLODNER LEARNING TO INTEGRATE SYNTAX AND SEMANTICS, Pages 179-190, WENDY G. LEHNERT How Do Machine-Learning Paradigms Fare in Language Acquisition?, Pages 191-197, URI ZERNIK The Acquisition of Polysemy, Pages 198-204, JAMES H. MARTIN Cirrus: an automated protocol analysis tool, Pages 205-217, KURT VANLEHN, STEVE GARLICK Scientific Theory Formation Through Analogical Inference, Pages 218-229, BRIAN FALKENHAINER Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning, Pages 230-241, Michael J. Pazzani THE ROLE OF ABSTRACTIONS IN LEARNING QUALITATIVE MODELS, Pages 242-255, Igor Mozetic Learning by Experimentation, Pages 256-266, JAIME G. CARBONELL, YOLANDA GIL Observation and Generalisation in a Simulated Robot World, Pages 267-273, CLAUDE SAMMUT, DAVID HUME Empirical and Analytic Discovery in IL, Pages 274-280, MICHAEL H. SIMS Combining many searches in the FAHRENHEIT discovery system, Pages 281-287, Jan M. Zytkow Causal Analysis and Inductive Learning, Pages 288-299, JOHN R. ANDERSON Varieties of Learning in Soar: 1987, Pages 300-311, David M. Steier, John E. Laird, Allen Newell, Paul S. Rosenbloom, Rex A. Flynn, Andrew Golding, Thad A. Polk, Olin G. Shivers, Amy Unruh, Gregg R. Yost Hill-Climbing Theories of Learning, Pages 312-323, PAT LANGLEY, JOHN H. GENNARI, WAYNE IBA Bias, Version Spaces and Valiant's Learning Framework, Pages 324-336, David Haussler Recent Results on Boolean Concept Learning, Pages 337-352, MICHAEL KEARNS, MING LI, LEONARD PITT, LESLIE G. VALIANT Machine Learning from Structured Objects, Pages 353-363, ROBERT E. STEPP A New Approach to Unsupervised Learning in Deterministic Environments, Pages 364-375, RONALD L. RIVEST, ROBERT E. SCHAPIRE Searching for Operational Concept Descriptions in BAR, MetaLEX, and EBG, Pages 376-382, JACK MOSTOW Explanation-Based Generalization as Resolution Theorem Proving, Pages 383-389, SMADAR T. KEDAR-CABELLI, L. THORNE MCCARTY Analogy and Single-Instance Generalization, Pages 390-397, STUART J. RUSSELL The Devolving Science of Machine Learning, Pages 398-401, BAT GANGLY AUTHOR INDEX, Page 403