On generalization by neural networks

We report new results on the corner classification approach to training feedforward 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 pa-per 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 valu-able for real-time applications