Value-at-risk support vector machine: stability to outliers

  title={Value-at-risk support vector machine: stability to outliers},
  author={Peter Tsyurmasto and Michael Zabarankin and Stan Uryasev},
  journal={J. Comb. Optim.},
A support vector machine (SVM) stable to data outliers is proposed in three closely related formulations, and relationships between those formulations are established. The SVM is based on the value-at-risk (VaR) measure, which discards a specified percentage of data viewed as outliers (extreme samples), and is referred to as VaR-SVM. Computational experiments show that compared to the ν-SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers. 
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