1. A computer-implemented method for classifying a pattern into one of two classes, a class-of-interest or a class-other, comprising: receiving a set of labeled patterns from a class-of-interest, a set of unlabeled patterns from an input data set, and an estimate of the a priori probability of said class-of-interest in said input data set, said input-data-set being at least one of an image, video or speech data set; estimating a within-class scatter matrix; estimating a between-class scatter matrix; computing the eigenvalues and eigenvectors for an adaptive Fisher's criterion; selecting an eigenvector with the largest eigenvalue as the adaptive Fisher's discriminant projection vector; projecting each labeled pattern in said set of labeled patterns from said class-of-interest and each unlabeled pattern in said set of unlabeled patterns from said input data set onto said adaptive Fisher's discriminant projection vector to provide a set of labeled scalar measurements for said class-of-interest patterns and a set of unlabeled scalar measurements for the input data set patterns; executing a training stage using said a priori probability of said class-of-interest in said input data set, said set of labeled scalar measurements for said class-of-interest patterns, and said set of unlabeled scalar measurements for said input data set patterns, to estimate parameters for a function providing a least squares approximation of a class-of-interest posterior probability function; classifying said pattern into one of two classes, a class-of-interest or a class-other, in accordance with a conditional test defined by an adaptive Bayes decision rule; and wherein said pattern is classified using an optimal Fisher's linear decision boundary as either said class-of-interest or as said class-other by projecting said pattern onto said adaptive Fisher's discriminant projection vector and classifying said pattern as either said class-of-interest or as said class-other using said adaptive Bayes decision rule, without prior knowledge of said class-other.