1. A computer-implemented method of classifying a pattern into one of two classes, a class-of-interest and a class-other, comprising the steps of: receiving a training set of class-of-interest patterns, 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 any image, video or speech data set; selecting a predetermined number of Gaussian kernel densities functions; selecting parameter values for said Gaussian kernel densities functions where said selected parameter values cause said Gaussian kernel densities to approximate the probability density function of said input-data-set; executing a training stage using said a priori probability of said class-of-interest, said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, said training stage including a step of least squares approximation of a class-of-interest posterior distribution function using a linear combination of weighted said Gaussian kernel density functions; classifying said pattern from said input-data-set as being either said class-of-interest or said class-other in accordance with a conditional test defined by a adaptive Bayes decision rule; and wherein said pattern from said input-data-set is classified with minimum error as said class-of-interest or said class-other using said adaptive Bayes decision rule, where said adaptive Bayes decision rule is defined using said a priori probability of said class-of-interest, said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, and without any a priori knowledge of said class-other.
1. A computer-implemented method of classifying a pattern into one of two classes, a class-of-interest and a class-other, comprising the steps of: receiving a training set of class-of-interest patterns, 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 any image, video or speech data set; selecting a predetermined number of Gaussian kernel densities functions; selecting parameter values for said Gaussian kernel densities functions where said selected parameter values cause said Gaussian kernel densities to approximate the probability density function of said input-data-set; executing a training stage using said a priori probability of said class-of-interest, said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, said training stage including a step of least squares approximation of a class-of-interest posterior distribution function using a linear combination of weighted said Gaussian kernel density functions; classifying said pattern from said input-data-set as being either said class-of-interest or said class-other in accordance with a conditional test defined by a adaptive Bayes decision rule; and wherein said pattern from said input-data-set is classified with minimum error as said class-of-interest or said class-other using said adaptive Bayes decision rule, where said adaptive Bayes decision rule is defined using said a priori probability of said class-of-interest, said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, and without any a priori knowledge of said class-other. 7. The method of claim 1 wherein said step of classifying said pattern from said input-data-set as being said class-of-interest or said class-other includes a step of estimating a value for said class-of-interest posterior probability, {circumflex over (P)}(C int /X), for said pattern using the expression 
 {circumflex over (P)} ( C int /X )≅ A T F ( X )  (99) where A T ⁢ F ⁡ ( X ) = a 1 ⁢ P ⁡ ( X / k 1 ) P ⁡ ( X ) + a 2 ⁢ P ⁡ ( X / k 2 ) P ⁡ ( X ) ⁢ ⁢ … + a R ⁢ P ⁡ ( X / k R ) P ⁡ ( X ) ( 100 ) and A T F(X) is the said estimated value of said class-of-interest posterior probability, {circumflex over (P)}(C int /X).