期刊
JOURNAL OF COMBINATORIAL OPTIMIZATION
卷 28, 期 1, 页码 218-232出版社
SPRINGER
DOI: 10.1007/s10878-013-9678-9
关键词
Support vector machine; Classification; Conditional value-at-risk; Value-at-risk; Risk management; Optimization
资金
- AFOSR, New Developments in Uncertainty: Linking Risk Management, Reliability, Statistics and Stochastic Optimization [FA9550-11-1-0258]
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 -SVM. Computational experiments show that compared to the -SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers.
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