An information criterion for optimal neural network selection

Abstract

The choice of an optimal neural network design for a given problem is addressed. A relationship between optimal network design and statistical model identification is described. A derivative of Akaike's information criterion (AIC) is given. This modification yields an information statistic which can be used to objectively select a ;best' network for binary classification problems. The technique can be extended to problems with an arbitrary number of classes.