Increasing virtual samples through loss smoothness determination in large geometric margin minimum classification error training
Tsukasa Ohashi, Hideyuki Watanabe, Jun’ichi Tokuno, Shigeru Katagiri, Miho Ohsaki, Shigeki Matsuda, Hideki Kashioka · 2012
We propose a new method for automatically determining the smoothness of smooth classification error count loss for the recent Large Geometric Margin Minimum Classification Error (LGM-MCE) training. The method uses the Parzen-estimation-based formalization of MCE training, and it realizes the determination through the maximum likelihood estimation of error count risk in the one-dimensional geometric-margin-based misclassification measure. In the LGM-MCE framework, increase in the loss smoothness directly leads to an effect of producing virtual samples, which are expected to increase the training robustness to unseen samples. Focusing on this point, we also theoretically clarify the mechanism of this virtual sample generation. Through experiments, the utility of the proposed smoothness determination method is demonstrated, and the mechanism of producing virtual samples and its effect in robustness increase are also clearly illustrated.