  In this paper we consider the possibility of computing rather than training
the decision layer weights of a neural classifier. Such a possibility arises in
two way, from making an appropriate choice of loss function and by solving a
problem of constrained optimization. The latter formulation leads to a
promising new learning process for pre-decision weights with both simplicity
and efficacy.
