A novel approach to fault classification using sparse sets of exemplars

An algorithm is proposed for determining if a pattern classifier/recognizer can be developed based upon a sparse set of exemplars. Specifically, we address fault classifications issues associated with cable television distribution networks and use signatures of observed faults to train our neural networks. Our focus is to derive a training set of exemplars which will ensure that the training of a neural network classifier will result in a system capable of generalization.