On Minimax Training of Signal Discrimination

The article considers optimization of training and self-training of decision-making under conditions of a priori indeterminancy with a finite training-sample size and a minimax criterion. A solution is given for the problem of synthesizing locally minimax decision rules for discriminating between two constant signals with unknown amplitudes against a background of Gaussian noise with unknown variance when there are classified and unclassified training samples.