Automatic loss smoothness determination for minimum classification error training

The loss function smoothness embedded in the Minimum Classification Error (MCE) formalization increases the virtual training samples that lead to the optimal, minimum classification error status over unseen testing samples as well as given training samples. However, no rational method for finding the smoothness that corresponds to the optimal status has been developed yet. To alleviate this problem, we propose in this paper a new MCE training method that incorporates loss smoothness control based on the Parzen estimation of the classification error probability. Experiments clearly demonstrate the high utility of our proposed method.