Pattern classification through fuzzy likelihood

Pattern classification through fuzzy likelihood

Blog Article

This paper introduces a novel way to compute the membership function of a fuzzy set approximating the distribution of some observed data starting with their histogram.This membership function is in turn used to Pant 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 Tools dominance of the a posteriori probability of a class over another for a given observed value.

In this way a crisp classification is eventually obtained.