ON THE BEHAVIOR OF ARTIFICIAL NEURAL-NETWORK CLASSIFIERS IN HIGH-DIMENSIONAL SPACES

It is widely believed in the pattern recognition field that when a fix ed number of training samples is used to design a classifier, the gene ralization error of the classifier tends to increase as the number of features gets large. In this paper, we will discuss the generalization error of the artificial neural network (ANN) classifiers in high-dime nsional spaces, under a practical condition that the ratio of the trai ning sample size to the dimensionality is small. Experimental results show that the generalization error of ANN classifiers seems much less sensitive to the feature size than 1-NN, Parzen and quadratic classifi ers.