Selected Applications in Classifier Design

  • James C. Bezdek
Part of the Advanced Applications in Pattern Recognition book series (AAPR)


In this final chapter we consider several fuzzy algorithms that effect partitions of feature space ℝ p , enabling classification of unlabeled (future) observations, based on the decision functions which characterize the classifier. S25 describes the general problem in terms of a canonical classifier, and briefly discusses Bayesian statistical decision theory. In S26 estimation of the parameters of a mixed multivariate normal distribution via statistical (maximum likelihood) and fuzzy (c-means) methods is illustrated. Both methods generate very similar estimates of the optimal Bayesian classifier. S27 considers the utilization of the prototypical means generated by (A11.1) for characterization of a (single) nearest prototype classifier, and compares its empirical performance to the well-known k-nearest-neighbor family of deterministic classifiers. In S28, an implicit classifier design based on Ruspini’s algorithm is discussed and exemplified.


Unlabeled Data Decision Region Bayesian Classifier Classifier Design Maximum Classifier 
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

© Plenum Press, New York 1981

Authors and Affiliations

  • James C. Bezdek
    • 1
  1. 1.Utah State UniversityLoganUSA

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