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Classification can be seen as a mapping problem where some function of xn predicts the expectation of a class variable yn. This paper uses kernel methods for the prediction of class variable, together with a recently proposed cost function for classification, called Correntropy-loss(C-loss) function. C-Loss is a non-convex loss function based on a similarity measure called correntropy and is known to closely approximate the ideal 0 − 1 loss function for classification. This paper shows viadoi:10.1109/ijcnn.2012.6252721 dblp:conf/ijcnn/PokharelP12 fatcat:4etjnodwsncytcnsx77ishdoy4