Minimum classification error training with automatic setting of loss smoothness
Hideyuki Watanabe, Jun’ichi Tokuno, Tsukasa Ohashi, Shigeru Katagiri, Miho Ohsaki · 2011
The loss function smoothness embedded in the Minimum Classification Error formalization increases the number of virtual training samples, enables high robustness to unseen samples, and well approximates the ultimate, minimum classification error probability status. However, a rational method for controlling smoothness has not yet been developed. To alleviate this long-standing problem, we propose a new method that automatically sets the loss function smoothness through Parzen kernel (window) width estimation with a cross-validation maximum likelihood method. Experiments clearly show our proposed method's high utility.