Learning algorithms for feedforward neural networks based on classical and initial-scaling quasi-Newton methods

Abstract

This paper describes a set of feedforward neural network learning algorithms based on classical quasi-Newton optimization techniques which are demonstrated to be up to two orders of magnitude faster than backward-propagation. Then, through initial scaling of the inverse Hessian approximate, which makes the quasi-Newton algorithms invariant to scaling of the objective function, the learning performance is further improved. Simulations show that initial scaling improves the rate of learning of quasi-Newton-based algorithms by up to 50%. Overall, more than two to three orders of magnitude improvement is achieved compared to backward-propagation. Finally, the best of these learning methods is used in developing a small writer-dependent online handwriting recognizer for digits (0 through 9). The recognizer labels the training data correctly with an accuracy of 96.66%.