Based on adversarial game formulation proposed in [16], the authors show that the adversarial game formulation is equivalent to an empirical risk minimisation where the loss function is a point wise maximum of 2 { Y } cost functions ( Y is the number of classes). The authors then proved that this loss is Fisher consistent, and archives comparable empirical results to existing methods that are not Fisher consistent, and significantly outperforms the existing consistent method. My main concern is whether the contribution is significant enough. The main result appears to be relating the adversarial game formulation proposed in [16] to the ERM framework (of a rather complicated loss function), and it does appear incremental to me. A second issue I have is that unlike ERM, the "adversarial game" formulation has not been a standard scheme in the machine learning field yet, and more effort may be useful to convince the readers (this reviewer at least) that this is a well motivated framework as opposed to being ad hoc.