Computing the Solution Path for the Regularized Support Vector Regression

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

Authors

Lacey Gunter, Ji Zhu

Abstract

In this paper we derive an algorithm that computes the entire solu- tion path of the support vector regression, with essentially the same computational cost as fitting one SVR model. We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.