Fuzzy Classifier Design
By: by Ludmila I. Kuncheva
Series: Studies in Fuzziness and Soft Computing -- 49; Studies in Fuzziness and Soft Computing -- 49
Publisher: Physica-Verlag HD; Imprint: Physica
Published: 2000
Place of publication: Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 315
ISBN-13: 9783790824728; 9783790818505
Subjects: Optical pattern recognition; Computer science; Artificial intelligence; Management information systems
Description: 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.
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Mode of access: World Wide Web.
Table of contents: title: Introduction; title: Statistical Pattern Recognition; title: Statistical Classifiers; title: Fuzzy Sets; title: Fuzzy If-then Classifiers; title: Training of Fuzzy If-then Classifiers; title: Non If-then Classifiers; title: Combinations of Multiple Classifiers Using Fuzzy Sets; title: Conclusions: What to Choose? The complete table of contents can be found on the Internet: http://www.springer.de.