On the upper bound of the probability of error of a linear pattern classifier for probabilistic pattern classes

Given a set of sample patterns for two pattern classes, some simple expressions for the upper bound of the probability of error for a linear pattern classifier and the optimal linear discriminant function minimizing the upper bound are obtained. Using these results, if the tolerable probability of error of classifying patterns in the two pattern classes is not smaller than this upper bound, not only a linear pattern classifier is known to be feasible, but also a satisfactory linear discriminant function is given. The results presented here are independent of the probability distribution of the patterns in the pattern classes. For some special cases, a smaller upper bound is found.