1-Norm Support Vector Machines
Abstract
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.
Cite
Text
Zhu et al. "1-Norm Support Vector Machines." Neural Information Processing Systems, 2003.Markdown
[Zhu et al. "1-Norm Support Vector Machines." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/zhu2003neurips-1norm/)BibTeX
@inproceedings{zhu2003neurips-1norm,
title = {{1-Norm Support Vector Machines}},
author = {Zhu, Ji and Rosset, Saharon and Tibshirani, Robert and Hastie, Trevor J.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {49-56},
url = {https://mlanthology.org/neurips/2003/zhu2003neurips-1norm/}
}