Training multilayer perceptron classifiers based on a modified support vector method

In this paper we describe a training method for one hidden layer multilayer perceptron classifier which is based on the idea of support vector machines (SVM's). An upper bound on the Vapnik-Chervonenkis (VC) dimension is iteratively minimized over the interconnection matrix of the hidden layer and its bias vector. The output weights are determined according to the support vector method, but without making use of the classifier form which is related to Mercer's condition. The method is illustrated on a two-spiral classification problem.