Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 2014, Vol. 26, No. 11, Pages 2541-2569
(doi: 10.1162/NECO_a_00647)
@ 2014 Massachusetts Institute of Technology
Extended Robust Support Vector Machine Based on Financial Risk Minimization
Article PDF (898.75 KB)
Abstract

Financial risk measures have been used recently in machine learning. For example, v-support vector machine (v-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than v-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM’s possibility of achieving a better prediction performance with proper parameter setting.