Advertisement

Introduction

  • Ludmila I. Kuncheva
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 49)

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.

Keywords

Fuzzy Cluster Learn Vector Quantization Fuzzy Classifier Fuzzy ARTMAP Classifier Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ludmila I. Kuncheva
    • 1
  1. 1.School of InformaticsUniversity of WalesBangor GwyneddUK

Personalised recommendations