Effects of the sample size in artificial neural network classifier design

Discusses the effects of the sample size on the estimates of the error rate of the artificial neural network (ANN) classifiers. Experimental results show that the standard deviation of the estimated error rate of ANN classifiers is independent of the hidden unit size. In addition, it is shown that nevertheless the class distributions are Gaussian, ANN classifiers outperform the quadratic discriminant function when sizes of samples per class are much unequal.