The Entire Regularization Path for the Support Vector Machine

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Saharon Rosset, Robert Tibshirani, Ji Zhu, Trevor Hastie


In this paper we argue that the choice of the SVM cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model.