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 ε-accurate solution in O(log(1/ε)) 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.

Keywords

Coordinate descentSupport vector machineDescent (aeronautics)Dual (grammatical number)Computer scienceScale (ratio)Simple (philosophy)Coordinate systemAlgorithmMathematical optimizationArtificial intelligenceMathematicsEngineering

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Publication Info

Year
2008
Type
article
Pages
408-415
Citations
906
Access
Closed

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Cite This

Cho‐Jui Hsieh, Kai‐Wei Chang, Chih‐Jen Lin et al. (2008). A dual coordinate descent method for large-scale linear SVM. , 408-415. https://doi.org/10.1145/1390156.1390208

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DOI
10.1145/1390156.1390208