Fuzzy Classifier Design / Edition 1

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Overview

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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Product Details

  • ISBN-13: 9783790824728
  • Publisher: Physica-Verlag HD
  • Publication date: 12/15/2010
  • Series: Studies in Fuzziness and Soft Computing Series , #49
  • Edition description: Softcover reprint of hardcover 1st ed. 2000
  • Edition number: 1
  • Pages: 315
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

Table of Contents

Introduction: What are fuzzy classifiers?- The data sets used in this book.- Notations and acronyms.- Organization of the book.- Acknowledgements.- Statistical Pattern Recognition: Class, feature, feature space.- Classifier, discriminant functions, classification regions.- Clustering.- Prior probabilities, class-conditional probability density functions, posterior probabilities.- Minimum error and minimum risk classification. Loss matrix.- Performance estimation.- Experimental comparison of classifiers.- A taxonomy of classifier design methods.- Statistical Classifiers: Parametric classifiers.- Nonparametric classifiers.- Finding k-nn prototypes.- Neural networks.- Fuzzy Sets: Fuzzy logic, an oxymoron?- Basic definitions.- Operations on fuzzy sets.- Determining membership functions.- Fuzzy If-then Classifiers: Fuzzy if-then systems.- Function approximation with fuzzy if-then systems.- Fuzzy if-then classifiers.- Universal approximation and equivalences of fuzzy if-then classifiers.- Training of Fuzzy If-then Classifiers: Expert opinion or data analysis.- Tuning the consequents.- Tuning the antecedents.- Tuning antecedents and consequents using clustering.- Genetic algorithms for tuning fuzzy if-then classifiers.- Fuzzy classifiers and neural networks: hybridization or identity?- Forget interpretability and choose a model.- Non if-then Fuzzy Models: Early ideas.- Fuzzy k-nearest neighbors (k-nn) designs.- Generalized nearest prototype classifier (GNPC).- Combinations of Multiple Classifiers Using Fuzzy Sets: Combining classifiers: the variety of paradigms.- Classifier Selection.- Classifier Fusion.- Experimental results.- Conclusions: What to Choose.

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