Recognition of Normal Sets with Unknown and Different Means and Variances

An adaptive algorithm is developed for recognition of normal sets with unknown and different means and variances, which is based on utilization of classified training samples to form estimates of unknown parameters. The confidence of the recognition and the volumes of training and checking samples required for its support are estimated from the result of a statistical experiment using the Monte Carlo method.