Training of fuzzy if-then classifiers

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


Assume we have a difficult pattern recognition problem which can easily be handled by a human but not by a machine. Assume also that the human recognition process is difficult to articulate or express in any functional or algorithmic form. Examples of such tasks are face recognition and speaker verification. In some problems we have some knowledge about the classes. An example is handwriting recognition where the theoretical shapes, connections, loops, etc. for each symbol are known, so the “ideal” prototype for each class is described by a set of rules. Nevertheless, handwriting recognition by a machine (and sometimes by a human) is still a challenge. Two natural approaches to designing a classifier are
  • Ask an expert how they solve the problem and try to encapsulate the knowledge in a fuzzy rule-base classifier.

  • Collect input-output data (i.e., a labeled data set) and extract the classifier parameters from the data.


Membership Function Rule Base Fuzzy Model Speaker Verification Firing Strength 
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

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

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