Overview
The unified approach for extracting fuzzy rules against different fuzzy classifier architectures
A new learning paradigm for neural network classifiers based on the network synthesis principle
Extensive performance comparisons including conventional classifiers
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Table of contents (16 chapters)
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Front Matter
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Pattern Classification
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Front Matter
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Function Approximation
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Pattern Classification
Book Subtitle: Neuro-fuzzy Methods and Their Comparison
Authors: Shigeo Abe
DOI: https://doi.org/10.1007/978-1-4471-0285-4
Publisher: Springer London
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag London 2001
Hardcover ISBN: 978-1-85233-352-2Published: 11 December 2000
Softcover ISBN: 978-1-4471-1077-4Published: 04 October 2012
eBook ISBN: 978-1-4471-0285-4Published: 06 December 2012
Edition Number: 1
Number of Pages: XIX, 327
Topics: Artificial Intelligence, Complexity, Pattern Recognition
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