On stability of decision rules in the presence of errors in classification of the learning sample

Pattern recognition problems are considered for the case of inaccessible credible classification of the learning sample. The effect of errors in classification of the learning sample on the risk of the Bayes decision rule is studied in the case of estimating the maximal likelihood of the conditional density parameters. Analytically and numerically solvable decision rules stable with errors in the learning sample are proposed and studied.