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Classifier Design

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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Part of the book series: The Handbooks of Fuzzy Sets Series ((FSHS,volume 4))

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Abstract

In Section 1.1 we defined a classifier as any function D:ℜp ↦ Npc. The value y = D(z) is the label vector for z in ℜP. D is a crisp classifier if D [ℜp] = Nhc; otherwise, the classifier is fuzzy, possibilistic or probabilistic, which for convenience we lump together as soft classifiers. This chapter describes some of the most basic (and often most useful) classifier designs, along with some fuzzy generalizations and relatives.

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Authors and Affiliations

  1. University of West Florida, USA

    James C. Bezdek

  2. University of Missouri, USA

    James Keller

  3. Colorado School of Mines, USA

    Raghu Krisnapuram

  4. Indian Statistical Institute, India

    Nikhil R. Pal

Authors
  1. James C. Bezdek
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  2. James Keller
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  3. Raghu Krisnapuram
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  4. Nikhil R. Pal
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Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R. (1999). Classifier Design. In: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbooks of Fuzzy Sets Series, vol 4. Springer, Boston, MA. https://doi.org/10.1007/0-387-24579-0_4

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  • DOI: https://doi.org/10.1007/0-387-24579-0_4

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