Classifier conditional posterior probabilities

Abstract

Classifiers based on probability density estimates can be used to find posterior probabilities for the objects to be classified. These probabilities can be used for rejection or for combining classifiers. Posterior probabilities for other classifiers, however, have to be conditional for the classifier., i.e. they yield class probabilities for a given value of the classifier outcome instead for a given input feature vector. In this paper they are studied for a set of individual classifiers as well as for combination rules.