Leave-one-out Bounds for Support Vector Regression

The success of support vector machine (SVM) depends critically on the kernel and the parameters in it. One of the most reasonable approaches is to select the kernel and the parameters by minimizing the bound of leave-one-out (Loo) error. However, the computation of the Loo error is extremely time consuming. Therefore, an efficient strategy is to minimize an upper bound of the Loo error, instead of the error itself. In fact, for support vector classification (SVC), some famous bounds have been proposed. This paper is concerned with support vector regression (SVR). We derive two Loo bounds for two algorithms of SVR. In order to show the validity, preliminary experiments are also presented