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By minimizing the conditional expectation of random loss in the 1−β worst case, the performance of the ν-support vector machine SVM severely depends on its assumption on the underlying distribution. This paper proposes a robust ν-SVM based on worst-case conditional value-at-risk WCCVaR minimization, which assumes that the underlying distribution comes from a certain set of potential distributions and minimizes the maximum CVaR associated with these distributions. The problem can be transformed into a quadratic programme and handle nonlinear classification problems. Experiments on six data sets clearly show that the robust approach can achieve superior results than the ν-SVM.