An hybrid architecture for active and incremental learning: the self-organizing perceptron (SOP) network

This paper describes a new hybrid architecture for an artificial neural network classifier that enables incremental learning. The learning algorithm of the proposed architecture detects the occurrence of unknown data and automatically adapts the structure of the network to learn these new data, without degrading previous knowledge. The architecture combines an unsupervised self-organizing map with a supervised perceptron network to form the self-organizing perceptron network