A Leave-One-Out Bound for ν-Support Vector Regression

摘要

An upper bound on the Leave-one-out (Loo) error for ν-support vector regression (ν-SVR) is presented. This bound is based on the geometrical concept of span. We can select the parameters of ν-SVR by minimizing this upper bound instead of the error itself, because the computation of the Loo error is extremely time consuming. We also can estimate the generalization performance of ν-SVR with the help of the upper bound. It is shown that the bound presented herein provide informative and efficient approximations of the generalization behavior based on two data sets.