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Machine Learning

An Artificial Intelligence Approach

  • Ryszard S. Michalski
  • Jaime G. Carbonell
  • Tom M. Mitchell

Part of the Symbolic Computation book series (SYMBOLIC)

Table of contents

  1. Front Matter
    Pages i-xi
  2. General Issues in Machine Learning

    1. Front Matter
      Pages 1-1
    2. Jaime G. Carbonell, Ryszard S. Michalski, Tom M. Mitchell
      Pages 3-23
    3. Herbert A. Simon
      Pages 25-37
  3. Learning from Examples

    1. Front Matter
      Pages 39-39
    2. Thomas G. Dietterich, Ryszard S. Michalski
      Pages 41-81
    3. Ryszard S. Michalski
      Pages 83-134
  4. Learning in Problem-Solving and Planning

    1. Front Matter
      Pages 135-135
    2. Tom M. Mitchell, Paul E. Utgoff, Ranan Banerji
      Pages 163-190
    3. John R. Anderson
      Pages 191-219
    4. Frederick Hayes-Roth
      Pages 221-240
  5. Learning from Observation and Discovery

    1. Front Matter
      Pages 241-241
    2. Pat Langley, Gary L. Bradshaw, Herbert A. Simon
      Pages 307-329
    3. Ryszard S. Michalski, Robert E. Stepp
      Pages 331-363
  6. Learning from Instruction

    1. Front Matter
      Pages 365-365
  7. Applied Learning Systems

  8. Back Matter
    Pages 511-572

About this book

Introduction

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.

Keywords

Lernender Automat Mathematische Lerntheorie artificial intelligence behavior cognition epistemology intelligence learning machine learning modeling pattern recognition philosophy

Editors and affiliations

  • Ryszard S. Michalski
    • 1
  • Jaime G. Carbonell
    • 2
  • Tom M. Mitchell
    • 3
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.Carnegie-Mellon University PittsburghUSA
  3. 3.Rutgers University New BrunswickUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-12405-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 1983
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-12407-9
  • Online ISBN 978-3-662-12405-5
  • Buy this book on publisher's site