Neural networks as a tool to generate pattern classification algorithms

Extraction of algorithms from pattern classification neural networks is discussed. It is shown that input-to-hidden weights for three layers, or otherwise, hidden-to-hidden weights for four layers are determined so that corresponding hyperplanes separate all the training data in a class from the remaining classes. The extraction of a classification algorithm from a trained network is discussed, and its generalization ability is compared with that of the original network. Weights of the neural networks, generated by the backpropagation algorithm for number recognition, are tuned, and classification algorithms are demonstrated to be extracted