Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Ji Zhu, Saharon Rosset, Robert Tibshirani, Trevor Hastie
The standard 2-norm SVM is known for its good performance in two- In this paper, we consider the 1-norm SVM. We class classi£cation. argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an ef£cient algorithm that computes the whole solution path of the 1-norm SVM, hence facilitates adaptive selection of the tuning parameter for the 1-norm SVM.