| We propose a two-class linear classification model by taking into account the Euclidean distance from each data point to the discriminant hyperplane and introducing a risk measure which is known as the conditional value-at-risk in financial risk management. It is formulated as a nonconvex programming problem and we present a solution method for obtaining either a globally or a locally optimal solution by examining the special structure of the problem. Also, this model is proved to be equivalent to the $\nu$-support vector classification under some parameter setting, and numerical experiments show that the proposed model has better predictive accuracy in general. |
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