A novel multilayer neural networks training algorithm that minimizes the probability of classification error

A multilayer neural networks training algorithm that minimizes the probability of classification error is proposed. The claim is made that such an algorithm possesses some clear advantages over the standard backpropagation (BP) algorithm. The convergence analysis of the proposed procedure is performed and convergence of the sequence of criterion realizations with probability of one is proven. An experimental comparison with the BP algorithm on three artificial pattern recognition problems is given.