1. A method for robust pattern recognition, comprising the steps of: (a) generating N sets of feature vectors x 1, x 2,... x N from a set of observation vectors which are indicative of a pattern which it is desired to recognize, at least one of said sets of feature vectors being different than at least one other of said sets of feature vectors and being preselected for purposes of containing at least some complimentary information with regard to said at least one other of said sets of feature vectors; and (b) combining said N sets of feature vectors in a manner to obtain an optimized set of feature vectors which best represents said pattern, said combining being performed in accordance with the equation: p ( x 1, x 2,... x N &verbar;s j )&equals; f — n &lcub;K&plus;&lsqb;w 1 p ( x 1 &verbar;s j ) q &plus;w 2 p ( x 2 &verbar;s j ) q &plus;... &plus;w N p ( x N &verbar;s j ) q &rsqb; 1/q &rcub; where: f — n is one of an exponential function exp( ) and a logarithmic function log( ), s j is a label for a class j, N is greater than or equal to 2, p(x 1, x 2,... x N &verbar;s j ) is conditional probability of feature vectors x 1, x 2,... x N given that they are generated by said class j, K is a normalization constant, w 1, w 2,... w N are weights assigned to x 1, x 2,... x N respectively according to confidence levels therein; and q is a real number corresponding to a desired combination function.