A Dual Coordinate Descent Method for Large-scale Linear SVM
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BibTeX
@MISC{Hsieh_adual,
author = {Cho-jui Hsieh and Kai-wei Chang and Chih-jen Lin and S. Sathiya Keerthi},
title = {A Dual Coordinate Descent Method for Large-scale Linear SVM},
year = {}
}
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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 L2loss functions. The proposed method is simple and reaches an ɛ-accurate solution in O(log(1/ɛ)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVM perf, and a recent primal coordinate descent implementation. 1.
