1-norm Support Vector Machines

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The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We 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.

Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibs

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1-norm Svm | NIPS 2003 | NIPS 2007 | Redundant Noise Features | Standard 2-norm Svm |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2003
Where	NIPS
Authors	Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani

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1-norm Support Vector Machines

1-norm Svm | NIPS 2003 | NIPS 2007 | Redundant Noise Features | Standard 2-norm Svm |

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