Bayes estimation on parameters of the single-class classifier

Abstract

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.