Proceedings of the Fourth International Workshop on MACHINE LEARNING - 1st Edition - ISBN: 9780934613415, 9781483282855

Proceedings of the Fourth International Workshop on MACHINE LEARNING

1st Edition

June 22–25, 1987 University of California, Irvine

Editors: Pat Langley
eBook ISBN: 9781483282855
Imprint: Morgan Kaufmann
Published Date: 1st September 1998
Page Count: 410
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Description

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.

Table of Contents


Preface: The Emerging Science of Machine Learning


Learning and Classification Exemplar-Based Approaches


Learning About Speech Sounds: The NEXUS Project


PROTOS: An Exemplar-Based Learning Apprentice


Learning Representative Exemplars of Concepts: An Initial Case Study


Probabilistic Approaches


Decision Trees as Probabilistic Classifiers


Conceptual Clustering, Learning from Examples, and Inference


How to Learn Imprecise Concepts: A Method for Employing a Two-Tiered Knowledge Representation in Learning


Quasi-Darwinian Learning in a Classifier System


Concept Learning and Bias


More Robust Concept Learning Using Dynamically-Variable Bias


Incremental Adjustment of Representations for Learning


Learning, Problem Solving, and Planning Heuristic Search Approaches


Concept Learning in Context


Strategy Learning with Multilayer Connectionist Representations


Learning a Preference Predicate


Planning Approaches


Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System


The Anatomy of a Weak Learning Method for Use in Goal Directed Search


Learning and Reusing Explanations


Problem Reduction Approaches


LT Revisited: Experimental Results of Applying Explanation-Based Learning to the Logic of Principia Mathematica


What is an Explanation in DISCIPLE?


Extending Problem Solver Capabilities Through Case-Based Inference


Learning and Natural Language


Learning to Integrate Syntax and Semantics


How Do Machine-Learning Paradigms Fare in Language Acquisition?


The Acquisition of Polysemy


Machine Discovery Observational Discovery


Cirrus: An Automated Protocol Analysis Tool


Scienti

Details

No. of pages:
410
Language:
English
Copyright:
© Morgan Kaufmann 1987
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9781483282855

About the Editor

Pat Langley

Ratings and Reviews