Cross-validation without a validation set in BP-trained neural nets

Generalization in backprop-trained multilayer neural networks is discussed for problems where training data is either scarce or else extremely costly to obtain. In this case, the usual method of cross-validation, whereby the data set is partitioned into training, testing, and validation sets, is not feasible. In this paper we demonstrate that it is sometimes possible to use all available data for training a large network (a network capable of overfitting the data) and yet still determine an appropriate stopping point to ensure that the network generalizes properly