Pattern Classification: Neuro-fuzzy Methods and Their Comparison / Edition 1

by Shigeo Abe

Overview

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.

Product Details

ISBN-13:	9781852333522
Publisher:	Springer London
Publication date:	01/25/2001
Edition description:	2001
Pages:	327
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

I. Pattern Classification.- 1. Introduction.- 2. Multilayer Neural Network Classifiers.- 3. Support Vector Machines.- 4. Membership Functions.- 5. Static Fuzzy Rule Generation.- 6. Clustering.- 7. Tuning of Membership Functions.- 8. Robust Pattern Classification.- 9. Dynamic Fuzzy Rule Generation.- 10. Comparison of Classifier Performance.- 11. Optimizing Features.- 12. Generation of Training and Test Data Sets.- II. Function Approximation.- 13. Introduction.- 14. Fuzzy Rule Representation and Inference.- 15. Fuzzy Rule Generation.- 16. Robust Function Approximation.- III. Appendices.- A. Conventional Classifiers.- A.1 Bayesian Classifiers.- A.2 Nearest Neighbor Classifiers.- A.2.1 Classifier Architecture.- A.2.2 Performance Evaluation.- B. Matrices.- B.1 Matrix Properties.- B.2 Least-squares Method and Singular Value Decomposition.- B.3 Covariance Matrix.- References.