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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...
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