A Forward Constrained Selection Algorithm for Probabilistic Neural Network

Authors:
Ning Zong;Xia Hong
Affiliations:
School of Systems Engineering, University of Reading, RG6 6AY, UK;School of Systems Engineering, University of Reading, RG6 6AY, UK
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples and the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.