Overview

This book about fuzzy classifier design briefly introduces the fundamentals of supervised pattern recognition and fuzzy set theory. Fuzzy if-then classifiers are defined and some theoretical properties thereof are studied. Popular training algorithms are detailed. Non if-then fuzzy classifiers include relational, k-nearest neighbor, prototype-based designs, etc. A chapter on multiple classifier combination discusses fuzzy and non-fuzzy models for fusion and selection.

Full Product Details

Author:   Ludmila I. Kuncheva
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   2000 ed.
Volume:   49
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   1.420kg
ISBN:  

9783790812985

ISBN 10:   3790812986
Pages:   315
Publication Date:   26 April 2000
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. Introduction.- 1.1 What are fuzzy classifiers?.- 1.2 The data sets used in this book.- 1.3 Notations and acronyms.- 1.4 Organization of the book.- 1.5 Acknowledgements.- 2. Statistical pattern recognition.- 2.1 Class, feature, feature space.- 2.2 Classifier, discriminant functions, classification regions.- 2.3 Clustering.- 2.4 Prior probabilities, class-conditional probability density functions, posterior probabilities.- 2.5 Minimum error and minimum risk classification. Loss matrix.- 2.6 Performance estimation.- 2.7 Experimental comparison of classifiers.- 2.8 A taxonomy of classifier design methods.- 3. Statistical classifiers.- 3.1 Parametric classifiers.- 3.2 Nonparametric classifiers.- 3.3 Finding k-nn prototypes.- 3.4 Neural networks.- 4. Fuzzy sets.- 4.1 Fuzzy logic, an oxymoron?.- 4.2 Basic definitions.- 4.3 Operations on fuzzy sets.- 4.4 Determining membership functions.- 5. Fuzzy if-then classifiers.- 5.1 Fuzzy if-then systems.- 5.2 Function approximation with fuzzy if-then systems.- 5.3 Fuzzy if-then classifiers.- 5.4 Universal approximation and equivalences of fuzzy if-then classifiers.- 6. Training of fuzzy if-then classifiers.- 6.1 Expert opinion or data analysis?.- 6.2 Tuning the consequents.- 6.3 Toning the antecedents.- 6.4 Tuning antecedents and consequents using clustering.- 6.5 Genetic algorithms for tuning fuzzy if-then classifiers.- 6.6 Fuzzy classifiers and neural networks: hybridization or identity?.- 6.7 Forget interpretability and choose a model.- 7. Non if-then fuzzy models.- 7.1 Early ideas.- 7.2 Fuzzy k-nearest neighbors (k-nn) designs.- 7.3 Generalized nearest prototype classifier (GNPC).- 8. Combinations of multiple classifiers using fuzzy sets.- 8.1 Combining classifiers: the variety of paradigms.- 8.2 Classifier selection.- 8.3 Classifier fusion.- 8.4 Experimental results.- 9. Conclusions: What to choose?.- A. Appendix: Numerical results.- A.1 Cone-torus data.- A.2 Normal mixtures data..- A.3 Phoneme data.- A.4 Satimage data.- References.

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