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Machine Learning of Inductive Bias

  • Paul E. Utgoff

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Paul E. Utgoff
    Pages 1-11
  3. Paul E. Utgoff
    Pages 12-21
  4. Paul E. Utgoff
    Pages 22-32
  5. Paul E. Utgoff
    Pages 33-44
  6. Paul E. Utgoff
    Pages 45-62
  7. Paul E. Utgoff
    Pages 63-90
  8. Paul E. Utgoff
    Pages 91-96
  9. Back Matter
    Pages 97-165

About this book

Introduction

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.

Keywords

grammar knowledge knowledge base learning machine learning

Authors and affiliations

  • Paul E. Utgoff
    • 1
  1. 1.University of MassachusettsAmherstUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-2283-2
  • Copyright Information Springer-Verlag US 1986
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-9408-5
  • Online ISBN 978-1-4613-2283-2
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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