Revisiting squared-error and cross-entropy functions for training neural network classifiers

This paper investigates the efficacy of cross-entropy and square-error objective functions used in training feed-forward neural networks to estimate posterior probabilities. Previous research has found no appreciable difference between neural network classifiers trained using cross-entropy or squared-error. The approach employed here, though, shows cross-entropy has significant, practical advantages over squared-error.