  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.
