A training scheme for pattern classification using multi-layer feed-forward neural networks

Abstract

This study highlights the subject of weight initialization in multi-layer feed-forward networks. Training data is analyzed and the notion of critical point is introduced for determining the initial weights for input to hidden layer synaptic connections. The proposed method has been applied to artificial data. Experimental results show that the proposed method takes almost half the training time required for standard backpropagation.