Fuzzy Sets in Approximate Reasoning and Information Systems

  • James C. Bezdek
  • Didier Dubois
  • Henri Prade

Part of the The Handbooks of Fuzzy Sets Series book series (FSHS, volume 5)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Introduction

  3. Part I Reasoning

    1. Front Matter
      Pages 15-15
    2. Bernadette Bouchon-Meunier, Didier Dubois, Lluis Godo, Henri Prade
      Pages 15-190
    3. Vilém. Novák
      Pages 191-241
    4. Loredana Biacino, Giangiacomo Gerla
      Pages 243-278
  4. Part II Learning and Fusion

    1. Front Matter
      Pages 279-279
    2. Bernadette Bouchon-Meunier, Christophe Marsala
      Pages 279-304
    3. Detlef Nauck, Rudolf Kruse
      Pages 305-334
    4. Didier Dubois, Henri Prade, Ronald Yager
      Pages 335-401
  5. Part III Fuzzy Information Systems

    1. Front Matter
      Pages 403-403
    2. Patrick Bosc, Bill B. Buckles, Frederick E. Petry, Olivier Pivert
      Pages 403-468
    3. Donald H. Kraft, Gloria Bordogna, Gabriella Pasi
      Pages 469-510

About this book


Approximate reasoning is a key motivation in fuzzy sets and possibility theory. This volume provides a coherent view of this field, and its impact on database research and information retrieval. First, the semantic foundations of approximate reasoning are presented. Special emphasis is given to the representation of fuzzy rules and specialized types of approximate reasoning. Then syntactic aspects of approximate reasoning are surveyed and the algebraic underpinnings of fuzzy consequence relations are presented and explained. The second part of the book is devoted to inductive and neuro-fuzzy methods for learning fuzzy rules. It also contains new material on the application of possibility theory to data fusion. The last part of the book surveys the growing literature on fuzzy information systems. Each chapter contains extensive bibliographical material.
Fuzzy Sets in Approximate Reasoning and Information Systems is a major source of information for research scholars and graduate students in computer science and artificial intelligence, interested in human information processing.


Fuzzy Information artificial intelligence fuzzy methods fuzzy sets information system intelligence learning set theory

Editors and affiliations

  • James C. Bezdek
    • 1
  • Didier Dubois
    • 2
  • Henri Prade
    • 2
  1. 1.University of West FloridaUSA
  2. 2.IRITCNRS & University of Toulouse IIIFrance

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 1999
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7390-2
  • Online ISBN 978-1-4615-5243-7
  • Series Print ISSN 1388-4352
  • Buy this book on publisher's site
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