Abstract
Fuzzy pattern recognition is sometimes identified with fuzzy clustering or with fuzzy if-then systems used as classifiers. In this book we adopt a broader view: fuzzy pattern recognition is about any pattern classification paradigm that involves fuzzy sets. To a certain extent fuzzy pattern recognition is dual to classical pattern recognition, as delineated in the early seventies by Duda and Hart [87], Fukunaga [100], Tou and Gonzalez [324], and thereby consists of three basic components: clustering, classifier design and feature selection [39] . Fuzzy clustering has been the most successful offspring of fuzzy pattern recognition so far. The fuzzy c-means algorithm devised by Bezdek [34] has admirable popularity in a great number of fields, both engineering and non-engineering. Fuzzy feature selection is virtually absent, or disguised as something else. This book is about the third component fuzzy classifier design.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kuncheva, L.I. (2000). Introduction. In: Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing, vol 49. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1850-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1850-5_1
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2472-8
Online ISBN: 978-3-7908-1850-5
eBook Packages: Springer Book Archive