ML Anthology

The Entire Regularization Path for the Support Vector Machine

Saharon Rosset, Robert Tibshirani, Ji Zhu, Trevor J. Hastie
NeurIPS 2004 pp. 561-568
/neurips/2004/rosset2004neurips-entire/

Abstract

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.

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Cite

Text

Rosset et al. "The Entire Regularization Path for the Support Vector Machine." Neural Information Processing Systems, 2004.

Markdown

[Rosset et al. "The Entire Regularization Path for the Support Vector Machine." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/rosset2004neurips-entire/)

BibTeX

@inproceedings{rosset2004neurips-entire,
  title     = {{The Entire Regularization Path for the Support Vector Machine}},
  author    = {Rosset, Saharon and Tibshirani, Robert and Zhu, Ji and Hastie, Trevor J.},
  booktitle = {Neural Information Processing Systems},
  year      = {2004},
  pages     = {561-568},
  url       = {https://mlanthology.org/neurips/2004/rosset2004neurips-entire/}
}