Selecting an optimal neural network

Abstract

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 select a best network for binary classification problems objectively. The technique can be extended to problems with an arbitrary number of classes.<>