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Foundations of Knowledge Acquisition

Machine Learning

  • Book
  • © 1993
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Overview

Editors:
  1. Alan L. Meyrowitz

    1. Naval Research Laboratory, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Susan Chipman

    1. Office of Naval Research, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 195)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xi
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  2. Learning = Inferencing + Memorizing

    • Ryszard S. Michalski
    Pages 1-41
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  3. Adaptive Inference

    • Alberto Segre, Charles Elkan, Daniel Scharstein, Geoffrey Gordon, Alexander Russell
    Pages 43-81
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  4. On Integrating Machine Learning with Planning

    • Gerald F. DeJong, Melinda T. Gervasio, Scott W. Bennett
    Pages 83-116
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  5. The Role of Self-Models in Learning to Plan

    • Gregg Collins, Lawrence Birnbaum, Bruce Krulwich, Michael Freed
    Pages 117-143
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  6. Learning Flexible Concepts Using a Two-Tiered Representation

    • R. S. Michalski, F. Bergadano, S. Matwin, J. Zhang
    Pages 145-202
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  7. Competition-Based Learning

    • John J. Grefenstette, Kenneth A. De Jong, William M. Spears
    Pages 203-225
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  8. Problem Solving via Analogical Retrieval and Analogical Search Control

    • Randolph Jones
    Pages 227-262
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  9. A View of Computational Learning Theory

    • Leslie G. Valiant
    Pages 263-289
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  10. The Probably Approximately Correct (PAC) and Other Learning Models

    • David Haussler, Manfred Warmuth
    Pages 291-312
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  11. On the Automated Discovery of Scientific Theories

    • Daniel Osherson, Scott Weinstein
    Pages 313-330
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  12. Back Matter

    Pages 331-334
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Keywords

About this book

One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made.

Editors and Affiliations

  • Naval Research Laboratory, USA

    Alan L. Meyrowitz

  • Office of Naval Research, USA

    Susan Chipman

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