Automatic loss smoothness determination for Large Geometric Margin Minimum Classification Error training

A Parzen-estimation-based smoothness determination method for smooth classification error count loss was successfully applied to the early Minimum Classification Error (MCE) training that used a functional-margin-based misclassification measure. In this study, we apply this loss smoothness determination method to the recent MCE framework that uses a geometric-margin-based misclassification measure, and experimentally demonstrate its high utility. Furthermore, we theoretically clarify how the loss smoothness set in the one-dimensional geometric-margin-based misclassification measure space produces virtual samples, which are expected to increase the training robustness to unseen samples, in a sample space that usually has high-dimension.