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

Authors:
M. Kline;L. Berardi
Affiliations:
Cameron School of Business, University of North Carolina, Wilmington, NC, USA;Graduate School of Management, Kent State University, 44221, Kent, OH, USA
Venue:
Neural Computing and Applications
Year:
2005

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Abstract

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.