Computing the Solution Path for the Regularized Support Vector Regression

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

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Lacey Gunter, Ji Zhu


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 ļ¬tting 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.