Simplified upper and lower bounds to the probability of error for general M-ary hypotheses pattern recognition are obtained. The bounds, applicable to general non-gaussian densities and especially mixture densities encountered in adaptive pattern recognition, are simple to calculate and hence valuable for on-line performance evaluation of pattern recognition system. Computer evaluation of the bounds, established their tight nature and computational simplicity. Based on the bounds, feature extraction criteria are derived for supervised as well as parametric adaptive pattern recognition. (Author)
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