Stability of decision rules in pattern recognition problems

The paper is concerned with data recognition with errors in specifying conditional distributions of observation probabilities. For a model of errors with a “contaminating” distribution and for a model with additive distortions of observations stable (robust) decision rules are found. Guaranteed values of risk for stable and Bayesian decision rules are determined and compared. Numerical results are given.