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Pattern Classification

Neuro-fuzzy Methods and Their Comparison

  • Shigeo¬†Abe

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Pattern Classification

    1. Front Matter
      Pages 1-1
    2. Shigeo Abe
      Pages 3-20
    3. Shigeo Abe
      Pages 21-46
    4. Shigeo Abe
      Pages 47-61
    5. Shigeo Abe
      Pages 63-80
    6. Shigeo Abe
      Pages 81-107
    7. Shigeo Abe
      Pages 109-118
    8. Shigeo Abe
      Pages 119-157
    9. Shigeo Abe
      Pages 159-175
    10. Shigeo Abe
      Pages 177-196
    11. Shigeo Abe
      Pages 197-204
    12. Shigeo Abe
      Pages 205-237
  3. Function Approximation

    1. Front Matter
      Pages 249-249
    2. Shigeo Abe
      Pages 251-255
    3. Shigeo Abe
      Pages 257-261
    4. Shigeo Abe
      Pages 263-286
    5. Shigeo Abe
      Pages 287-297
  4. Back Matter
    Pages 299-327

About this book

Introduction

Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems.
The book consists of two parts: Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified.
In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared.
This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks.

Keywords

Fuzzy Fuzzy function approximation Pattern classification Performance Support Vector Machine algorithms architecture artificial intelligence classification function approximation fuzzy classifiers fuzzy system learning multilayer neural networks

Authors and affiliations

  • Shigeo¬†Abe
    • 1
  1. 1.Graduate School of Science and TechnologyKobe University, RokkodaiNada, KobeJapan

Bibliographic information