A Fast Partitioning Algorithm and a Comparison of Binary Feedforward Neural Networks
S. A. J Keibek, G. T. Barkema, H.M.A. Andree, M.H.F. Savenije, A. Taal · Europhysics Letters (EPL) · 1992
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.