A Dual Coordinate Descent Method for Large-Scale Linear SVM
Abstract
In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1- and L2-loss functions. The proposed method is simple and reaches an epsilon-accurate solution in O(log (1/epsilon)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, Tron, svmperf, and a recent primal coordinate descent implementation.
Cite
Text
Hsieh et al. "A Dual Coordinate Descent Method for Large-Scale Linear SVM." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390208Markdown
[Hsieh et al. "A Dual Coordinate Descent Method for Large-Scale Linear SVM." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/hsieh2008icml-dual/) doi:10.1145/1390156.1390208BibTeX
@inproceedings{hsieh2008icml-dual,
title = {{A Dual Coordinate Descent Method for Large-Scale Linear SVM}},
author = {Hsieh, Cho-Jui and Chang, Kai-Wei and Lin, Chih-Jen and Keerthi, S. Sathiya and Sundararajan, S.},
booktitle = {International Conference on Machine Learning},
year = {2008},
pages = {408-415},
doi = {10.1145/1390156.1390208},
url = {https://mlanthology.org/icml/2008/hsieh2008icml-dual/}
}