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Fuzzy Classifier Design: Studies in Fuzziness and Soft Computing, cartea 49

Autor Ludmila I. Kuncheva
en Limba Engleză Hardback – 26 apr 2000
Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever since have been a center of many discussions, fervently admired and condemned. Both proponents and opponents consider the argu­ ments pointless because none of them would step back from their territory. And stiH, discussions burst out from a single sparkle like a conference pa­ per or a message on some fuzzy-mail newsgroup. Here is an excerpt from an e-mail messagepostedin1993tofuzzy-mail@vexpert. dbai. twvien. ac. at. by somebody who signed "Dave". , . . . Why then the "logic" in "fuzzy logic"? I don't think anyone has successfully used fuzzy sets for logical inference, nor do I think anyone wiH. In my admittedly neophyte opinion, "fuzzy logic" is a misnomer, an oxymoron. (1 would be delighted to be proven wrong on that. ) . . . I carne to the fuzzy literature with an open mind (and open wal­ let), high hopes and keen interest. I am very much disiHusioned with "fuzzy" per se, but I did happen across some extremely interesting things along the way. " Dave, thanks for the nice quote! Enthusiastic on the surface, are not many of us suspicious deep down? In some books and journals the word fuzzy is religiously avoided: fuzzy set theory is viewed as a second-hand cheap trick whose aim is nothing else but to devalue good classical theories and open up the way to lazy ignorants and newcomers.
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Specificații

ISBN-13: 9783790812985
ISBN-10: 3790812986
Pagini: 332
Ilustrații: X, 315 p.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.64 kg
Ediția:2000
Editura: Physica-Verlag HD
Colecția Physica
Seria Studies in Fuzziness and Soft Computing

Locul publicării:Heidelberg, Germany

Public țintă

Research

Cuprins

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.