Published on Wed Jul 24 2019

Dibyasundar Das, Deepak Ranjan Nayak, Ratnakar Dash, Banshidhar Majhi

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 proposed technique has an advantage over the back-propagation method in terms of iterations required.

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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.