http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
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.
Studies in Fuzziness and Soft Computing, 49