Pattern Classification: Neuro-fuzzy Methods and Their Comparison by Shigeo Abe (

Description: Pattern Classification by Shigeo Abe Provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through performance evaluation of real data sets. This book offers fresh learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Notes The unified approach for extracting fuzzy rules against different fuzzy classifier architecturesA new learning paradigm for neural network classifiers based on the network synthesis principleExtensive performance comparisons including conventional classifiers 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. Promotional Springer Book Archives Long Description 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. Feature 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 Details ISBN1852333529 Author Shigeo Abe Language English ISBN-10 1852333529 ISBN-13 9781852333522 Media Book Format Hardcover Imprint Springer London Ltd Subtitle Neuro-fuzzy Methods and Their Comparison Place of Publication England Country of Publication United Kingdom Birth 1947 Short Title PATTERN CLASSIFICATION 2001/E Pages 327 DOI 10.1007/b80907 AU Release Date 2000-12-11 NZ Release Date 2000-12-11 UK Release Date 2000-12-11 Publisher Springer London Ltd Edition Description 2001 ed. Year 2000 Edition 2001st Publication Date 2000-12-11 Alternative 9781447110774 DEWEY 006.4 Illustrations XIX, 327 p. Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:137613626;

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