Pattern classification through fuzzy likelihood

Pattern classification through fuzzy likelihood

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This paper introduces a novel way to compute the membership function of a fuzzy set mre93ll/a apple watch approximating the distribution of some observed data starting with their histogram.This membership function is in turn used to obtain a posteriori probability through a suitable version of the Bayesian formula.The ordering imposed by an overtaking relation between fuzzy numbers translates immediately into a dominance of the bostik universal primer pro a posteriori probability of a class over another for a given observed value.In this way a crisp classification is eventually obtained.