Binary classification by SVM based tree type neural networks

A technique for building a multilayer perceptron classifier network is presented. Initially, a single perceptron tries to correctly classify as many samples as possible. Misclassified samples are taken care of by adding as bias the output of up to two neurons to the parent neuron. The final classification boundary between the two disjoint half spaces at the output of the parent neuron is determined by a maximum margin classifier type SVM applied jointly to the training set of the parent neuron along with the correcting inputs from its child neuron(s). The growth of a branch in the network ceases when the terminal neuron is able to correctly classify all samples from its training set. No a priori assumptions need to be made regarding the number of neurons in the network or the kernel of the SVM classifier. Examples are presented to illustrate the effectiveness of the technique.