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SHIGEO ABE - Pattern Recognition: Neuro-Fuzzy Methods and Their Comparison | NEW
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eBay-Artikelnr.:136785637334
Artikelmerkmale
- Artikelzustand
- Series
- Signal Processing Library
- Features
- Revised
- ISBN
- 9781852333522
Über dieses Produkt
Product Identifiers
Publisher
Springer London, The Limited
ISBN-10
1852333529
ISBN-13
9781852333522
eBay Product ID (ePID)
1753313
Product Key Features
Number of Pages
Xix, 327 Pages
Publication Name
Pattern Classification : Neuro-Fuzzy Methods and Their Comparison
Language
English
Subject
Engineering (General), Intelligence (Ai) & Semantics, Neural Networks, System Theory, Computer Vision & Pattern Recognition
Publication Year
2000
Type
Textbook
Subject Area
Computers, Technology & Engineering, Science
Format
Hardcover
Dimensions
Item Height
0.4 in
Item Weight
52.2 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
00-044672
Dewey Edition
21
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.3/2
Table Of Content
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.
Synopsis
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., This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.
LC Classification Number
Q334-342
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