On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier

We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier. Specifically, we introduce the notion of consistency intensity to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer to the Bayes optimal classifier, enabling fair comparisons of the excess risks of different surrogate risk minimizers. The main result of the paper has practical implications including (1) showing that hinge loss is superior to logistic and exponential loss in the sense that its empirical minimizer converges faster to the Bayes optimal classifier and (2) guiding to modify surrogate loss functions to accelerate the convergence to the Bayes optimal classifier. UBTECH Sydney AI Centre and the School of Information Technologies in the Faculty of Engineering and Information Technologies at The University of Sydney, NSW, 2006, Australia, [email protected], [email protected], [email protected]