On generalization by neural networks

Abstract

We report new results on the corner classification approach to training feedback forward neural networks. It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization. The paper also deals with the relations between the number of separable regions and the size of the training set for a binary data network. Prescriptive learning can be particularly valuable for real-time applications.