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Soft Computing in Case Based Reasoning

  • Book
  • © 2001

Overview

Editors:
  1. Sankar K. Pal

    1. Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

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  2. Tharam S. Dillon

    1. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Melbourne, Australia

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  3. Daniel S. Yeung

    1. Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

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  • No other book covers this particular aspect of CBR; reported articles and related references are scarce so a comprehensive overview in one volume is timely

  • Introduces the basic concepts and theories

  • Includes extensive and exhaustive reports of real-life applications

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xix
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  2. A Tutorial on Case Based Reasoning

    1. A Tutorial on Case Based Reasoning

      • Julie Main, Tharam S. Dillon, Simon C. K. Shiu
      Pages 1-28

  3. Fuzzy Sets

    1. On the Notion of Similarity in Case Based Reasoning and Fuzzy Theory

      • Hans-Dieter Burkhard, Michael M. Richter
      Pages 29-45

    2. Formalizing Case Based Inference Using Fuzzy Rules

      • Eyke Höllermeier, Didier Dubois, Henri Prade
      Pages 47-72

  5. Genetic Algorithms

    1. A Genetic Algorithm and Growing Cell Structure Approach to Learning Case Retrieval Structures

      • Werner Dubitzky, Francisco Azuaje
      Pages 115-146

    2. An Architecture for Hybrid Creative Reasoning

      • Amilcar Cardoso, Ernesto Costa, Penousal Machado, Francisco C. Pereira, Paulo Gomes
      Pages 147-177

    3. Teacher: A Genetics Based System for Learning and Generalizing Heuristics

      • Benjamin W. Wah, Arthur Ieumwananonthachai
      Pages 179-211

  8. Back Matter

    Pages 367-372
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Keywords

About this book

Soft Computing in Case Based Reasoning demonstrates how various soft computing tools can be applied to design and develop methodologies and systems with case based reasoning for real-life decision-making or recognition problems.
Comprising contributions from experts from all over the world, it:
- Provides an introduction to CBR and soft computing, and the relevance of their integration
- Evaluates the strengths and weaknesses of CBR in its current form
- Presents recent developments and significant applications in domains such as data-mining, medical diagnosis, knowledge-based expert systems, banking, and forensic investigation
- Addresses new information on developing intelligent systems
This book will be of particular interest to graduate students and researchers in computer science, electrical engineering and information technology but it will also be of interest to researchers and practitioners in the fields of systems design, pattern recognition and data mining.

Editors and Affiliations

  • Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

    Sankar K. Pal

  • Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Melbourne, Australia

    Tharam S. Dillon

  • Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

    Daniel S. Yeung

Bibliographic Information

  • Book Title: Soft Computing in Case Based Reasoning

  • Editors: Sankar K. Pal, Tharam S. Dillon, Daniel S. Yeung

  • DOI: https://doi.org/10.1007/978-1-4471-0687-6

  • Publisher: Springer London

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag London Limited 2001

  • eBook ISBN: 978-1-4471-0687-6Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XIX, 372

  • Number of Illustrations: 18 b/w illustrations

  • Topics: Artificial Intelligence

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