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This paper introduces a novel Bayesian classification approach which uses fuzzy set theory. The proposed algorithm has two basic steps. The first step constructs clusters describing the sample data. In the second step classification is done by using a new Bayesian approach based on fuzzy membership functions obtained via the clusters from the first step.

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This page is a summary of: A Fuzzy Bayesian Classifier with Learned Mahalanobis Distance, International Journal of Intelligent Systems, June 2014, Wiley,
DOI: 10.1002/int.21659.
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