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Fuzzy Artmap Modifications for Intersecting Class Distributions

  • M. Blume
  • D. A. Van Blerkom
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 43)

Abstract

As originally defined, the Fuzzy ARTMAP algorithm performs poorly with intersecting class distributions, as commonly occur in real-world data. This chapter describes several modifications which eliminate the underlying category proliferation problem. The performance of the original and modified algorithms is demonstrated with examples from the speech and image understanding domains.

Keywords

Input Vector Choice Function Class Distribution Test Vector Image Annotation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • M. Blume
    • 1
  • D. A. Van Blerkom
    • 2
  1. 1.HNC Software, Inc.San DiegoUSA
  2. 2.Photobit CorporationPasadenaUSA

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