Machine Learning - 1st Edition - ISBN: 9781558602519, 9780080510569

Machine Learning

1st Edition

A Multistrategy Approach, Volume IV

Editors: Ryszard Michalski George Tecuci
eBook ISBN: 9780080510569
Hardcover ISBN: 9781558602519
Paperback ISBN: 9781493303526
Imprint: Morgan Kaufmann
Published Date: 9th February 1994
Page Count: 782
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Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

Table of Contents

Machine Learning: A Multistrategy Approach, Volume IV
Edited by Ryszard Michalski and Gheorghe Tecuci

    Preface, by Ryszard S. Michalski and Gheorghe Tecuci
    Part One General Issues

      Chapter 1 Inferential Theory of Learning: Developing Foundations for Multistrategy Learning, by Ryszard S. Michalski
      Chapter 2 The Fiction and Nonfiction of Features, by Edward J. Wisniewski and Douglas L. Medin
      Chapter 3 Induction and the Organization of Knowledge, by Yves Kodratoff
      Chapter 4 An Inference-Based Framework for Multistrategy Learning, by Gheorghe Tecuci

    Part Two Theory Revision

      Chapter 5 A Multistrategy Approach to Refinement, by Raymond J. Mooney and Dirk Ourston
      Chapter 6 Theory Completion Using Knowledge-based Learning, by Bradley L. Whitehall and Stephen C-Y. Lu
      Chapter 7 GEMINI: An Integration of Analytical and Emirical Learning, by Andrea P. Danyluk
      Chapter 8 Theory Revision by Analyzing Explanations and Prototypes, by Stan Matwina dn Boris Plante
      Chapter 9 Interactive Theory Revision, by Luc De Raedt and Maurice Bruynooghe

    Part Three Cooperative Integration

      Chapter 10 Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning, by Michael Pazzani
      Chapter 11 Balanced Cooperative Modeling, by Katharina Morik
      Chapter 12 WHY: A System That Learns Using Causal Models and Examples, by Cristina Baroglio, Marco Botta, and Lorenza Saitta
      Chapter 13 Introspective Reasoning Using Meta-Explanations for Multistrategy Learning, by Ashwin Ram and Michael Cox
      Chapter 14


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Morgan Kaufmann
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About the Editor

Ryszard Michalski

Affiliations and Expertise

George Mason University

George Tecuci

Ratings and Reviews