The effective capacity of multilayer feedforward network classifiers

Theoretical results on the capacity, or Vapnik-Chervonenkis dimension, of a multilayer feedforward (neural) network classifier leads to much larger training sets than is used in many applications. In this paper it is shown that the effective capacity, that takes into account the training rule, is much smaller than the upper bounds on the capacity that are derived from these theoretical considerations. The success of many network applications can thereby be understood from the restricted possibilities of the optimization technique that is used for training the network