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Learning from Good and Bad Data

  • Book
  • © 1988

Overview

Authors:
  1. Philip D. Laird

    1. NASA Ames Research Center, USA

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Identification in the Limit from Indifferent Teachers

    1. Front Matter

      Pages 1-1
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    2. The Identification Problem

      • Philip D. Laird
      Pages 3-26

    3. Identification by Refinement

      • Philip D. Laird
      Pages 27-53

    4. How to Work With Refinements

      • Philip D. Laird
      Pages 55-109

  3. Probabilistic Identification from Random Examples

    1. Front Matter

      Pages 111-111
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    2. Probabilistic Approximate Identification

      • Philip D. Laird
      Pages 113-134

    3. Identification from Noisy Examples

      • Philip D. Laird
      Pages 135-195

    4. Conclusions

      • Philip D. Laird
      Pages 197-200

  4. Back Matter

    Pages 201-211
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Keywords

About this book

This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us­ ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat­ ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.

Authors and Affiliations

  • NASA Ames Research Center, USA

    Philip D. Laird

Bibliographic Information

  • Book Title: Learning from Good and Bad Data

  • Authors: Philip D. Laird

  • Series Title: The Springer International Series in Engineering and Computer Science

  • DOI: https://doi.org/10.1007/978-1-4613-1685-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Kluwer Academic Publishers 1988

  • Hardcover ISBN: 978-0-89838-263-1Published: 31 March 1988

  • Softcover ISBN: 978-1-4612-8951-7Published: 05 October 2011

  • eBook ISBN: 978-1-4613-1685-5Published: 06 December 2012

  • Series ISSN: 0893-3405

  • Edition Number: 1

  • Number of Pages: XVIII, 212

  • Topics: Artificial Intelligence

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