Backward-Forward Algorithm: An Improvement towards Extreme Learning Machine

Abstract

The extreme learning machine needs a large number of hidden nodes to generalize a single hidden layer neural network for a given training data-set. The need for more number of hidden nodes suggests that the neural-network is memorizing rather than generalizing the model. Hence, a supervised learning method is described here that uses Moore-Penrose approximation to determine both input-weight and output-weight in two epochs, namely, backward-pass and forward-pass. The proposed technique has an advantage over the back-propagation method in terms of iterations required and is superior to the extreme learning machine in terms of the number of hidden units necessary for generalization.

12 Pages, 11 figures, to be submitted to journal