Proceedings of the Fourth International Workshop on MACHINE LEARNING

Proceedings of the Fourth International Workshop on MACHINE LEARNING

June 22–25, 1987 University of California, Irvine

1st Edition - September 1, 1998

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  • Editor: Pat Langley
  • eBook ISBN: 9781483282855

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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

    Scientific Theory Formation Through Analogical Inference

    Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning

    The Role of Abstractions in Learning Qualitative Models

    Discovery and Experimentation

    Learning by Experimentation

    Observation and Generalisation in a Simulated Robot World

    Empirical and Analytic Discovery in IL

    Combining Many Searches in the FAHRENHEIT Discovery System

    Cognitive Architectures for Learning

    Causal Analysis and Inductive Learning

    Varieties of Learning in Soar: 1987

    Hill-Climbing Theories of Learning


    Bias, Version Spaces and Valiant's Learning Framework

    Recent Results on Boolean Concept Learning

    Machine Learning from Structured Objects

    A New Approach to Unsupervised Learning in Deterministic Environments

    Searching for Operational Concept Descriptions in BAR, MetaLEX, and EBG

    Explanation-Based Generalization as Resolution Theorem Proving

    Analogy and Single-Instance Generalization


Product details

  • No. of pages: 410
  • Language: English
  • Copyright: © Morgan Kaufmann 1998
  • Published: September 1, 1998
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483282855

About the Editor

Pat Langley

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