A Fast Partitioning Algorithm and a Comparison of Binary Feedforward Neural Networks

Abstract

A comparison was carried out of several learning algorithms for training feedforward neural networks with linear threshold units. These learning algorithms do not require an a priori network architecture, but add neurons at will during training. The performance of these algorithms was compared by using training sets with a particular correlation of the input patterns over the full range of possible correlations. For binary input patterns we present a fast method for the selection of input patterns that can be identified by a single neuron. This method is not based on the perceptron learning rule.