Modified stochastic approximation to enhance unsupervised learning

By simple modifications of a decision-directed learning procedure, the regression curves of multidimensional stochastic approximation can be rotated further apart, leading to enhanced convergence properties. Results of a Monte Carlo simulation for a binary hypotheses testing problem are given which illustrates this faster convergence.