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Machine Learning

An Artificial Intelligence Approach

  • Book
  • © 1983

Overview

Editors:
  1. Ryszard S. Michalski

    1. University of Illinois at Urbana-Champaign, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Jaime G. Carbonell

    1. Carnegie-Mellon University Pittsburgh, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Tom M. Mitchell

    1. Rutgers University New Brunswick, USA

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Part of the book series: Symbolic Computation (SYMBOLIC)

Part of the book sub series: Artificial Intelligence (1064)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xi
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  2. General Issues in Machine Learning

    1. Front Matter

      Pages 1-1
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    2. An Overview of Machine Learning

      • Jaime G. Carbonell, Ryszard S. Michalski, Tom M. Mitchell
      Pages 3-23

    3. Why Should Machines Learn?

      • Herbert A. Simon
      Pages 25-37

  3. Learning from Examples

    1. Front Matter

      Pages 39-39
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    2. A Comparative Review of Selected Methods for Learning from Examples

      • Thomas G. Dietterich, Ryszard S. Michalski
      Pages 41-81

    3. A Theory and Methodology of Inductive Learning

      • Ryszard S. Michalski
      Pages 83-134

  4. Learning in Problem-Solving and Planning

    1. Front Matter

      Pages 135-135
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    2. Learning by Analogy: Formulating and Generalizing Plans from Past Experience

      • Jaime G. Carbonell
      Pages 137-161

    3. Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics

      • Tom M. Mitchell, Paul E. Utgoff, Ranan Banerji
      Pages 163-190

    4. Acquisition of Proof Skills in Geometry

      • John R. Anderson
      Pages 191-219

    5. Using Proofs and Refutations to Learn from Experience

      • Frederick Hayes-Roth
      Pages 221-240

  5. Learning from Observation and Discovery

    1. Front Matter

      Pages 241-241
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    2. The Role of Heuristics in Learning by Discovery: Three Case Studies

      • Douglas B. Lenat
      Pages 243-306

    3. Rediscovering Chemistry with the Bacon System

      • Pat Langley, Gary L. Bradshaw, Herbert A. Simon
      Pages 307-329

    4. Learning from Observation: Conceptual Clustering

      • Ryszard S. Michalski, Robert E. Stepp
      Pages 331-363

  6. Learning from Instruction

    1. Front Matter

      Pages 365-365
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    2. Machine Transformation of Advice Into a Heuristic Search Procedure

      • David Jack Mostow
      Pages 367-403

    3. Learning By Being Told: Acquiring Knowledge for Information Management

      • Norman Haas, Gary G. Hendrix
      Pages 405-427

    4. The Instructible Production System: A Retrospective Analysis

      • Michael D. Rychener
      Pages 429-459
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Keywords

About this book

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter­ national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

Editors and Affiliations

  • University of Illinois at Urbana-Champaign, USA

    Ryszard S. Michalski

  • Carnegie-Mellon University Pittsburgh, USA

    Jaime G. Carbonell

  • Rutgers University New Brunswick, USA

    Tom M. Mitchell

Bibliographic Information

  • Book Title: Machine Learning

  • Book Subtitle: An Artificial Intelligence Approach

  • Editors: Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell

  • Series Title: Symbolic Computation

  • DOI: https://doi.org/10.1007/978-3-662-12405-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1983

  • Softcover ISBN: 978-3-662-12407-9Published: 03 October 2013

  • eBook ISBN: 978-3-662-12405-5Published: 17 April 2013

  • Edition Number: 1

  • Number of Pages: XI, 572

  • Number of Illustrations: 25 b/w illustrations

  • Additional Information: Jointly published with Tioga Publishing Company, 1983

  • Topics: Artificial Intelligence, Machinery and Machine Elements

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