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Machine Learning of Inductive Bias

  • Book
  • © 1986

Overview

Authors:
  1. Paul E. Utgoff

    1. University of Massachusetts, Amherst, USA

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    • Paul E. Utgoff
    Pages 1-11

  3. Related Work

    • Paul E. Utgoff
    Pages 12-21

  4. Searching for a Better Bias

    • Paul E. Utgoff
    Pages 22-32

  5. LEX and STABB

    • Paul E. Utgoff
    Pages 33-44

  6. Least Disjunction

    • Paul E. Utgoff
    Pages 45-62

  7. Constraint Back-Propagation

    • Paul E. Utgoff
    Pages 63-90

  8. Conclusion

    • Paul E. Utgoff
    Pages 91-96

  9. Back Matter

    Pages 97-165
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Keywords

About this book

This book is based on the author's Ph.D. dissertation[56]. The the­ sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre­ pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor­ mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob­ servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir­ able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.

Authors and Affiliations

  • University of Massachusetts, Amherst, USA

    Paul E. Utgoff

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