Pattern Classification Using Stochastic Approximation Techniques

A nonparametric training procedure capable of processing an arbitrary sequence of patterns based on stochastic approximation techniques is considered. An acceleration scheme based on the adaptive Robbins-Monro procedure to increase the rate of convergence and to make the estimation asymptotically efficient is proposed. This scheme has been applied to the problem of recognition of handwritten characters.