Foundations of Knowledge Acquisition

Machine Learning

  • Alan L. Meyrowitz
  • Susan Chipman

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ryszard S. Michalski
    Pages 1-41
  3. Alberto Segre, Charles Elkan, Daniel Scharstein, Geoffrey Gordon, Alexander Russell
    Pages 43-81
  4. Gerald F. DeJong, Melinda T. Gervasio, Scott W. Bennett
    Pages 83-116
  5. Gregg Collins, Lawrence Birnbaum, Bruce Krulwich, Michael Freed
    Pages 117-143
  6. R. S. Michalski, F. Bergadano, S. Matwin, J. Zhang
    Pages 145-202
  7. John J. Grefenstette, Kenneth A. De Jong, William M. Spears
    Pages 203-225
  8. Leslie G. Valiant
    Pages 263-289
  9. David Haussler, Manfred Warmuth
    Pages 291-312
  10. Daniel Osherson, Scott Weinstein
    Pages 313-330
  11. Back Matter
    Pages 331-334

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.


algorithms artificial intelligence cognitive science genetic algorithms intelligence learning learning theory logical reasoning machine learning

Editors and affiliations

  • Alan L. Meyrowitz
    • 1
  • Susan Chipman
    • 2
  1. 1.Naval Research LaboratoryUSA
  2. 2.Office of Naval ResearchUSA

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 1993
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-7923-9278-1
  • Online ISBN 978-0-585-27366-2
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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