An algorithm for the learning of weights in discrimination functions using a priori constraints

We introduce a learning algorithm for the weights in a very common class of discrimination functions usually called "weighted average." The learning algorithm can reduce the number of free variables by simple but effective a priori criteria about significant features. Here we apply our algorithm to three tasks of different dimensionality all concerned with face recognition.