Distributed coding for data representation of back-propagation neural network classifiers

Abstract

A new distributed input coding is derived by distributing the feature variables over a number of input nodes based on the distribution of the training data. Using this coding method representation, the range of each input node will be fully optimised; this enables the network to converge at a higher rate during training. The coding method also enables the network to maintain the generalisation capability of conventional normalisation coding.