A dual coordinate descent method for large-scale linear SVM

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1 January 2008

proceedings article
Published by Association for Computing Machinery (ACM)

p. 408-415
https://doi.org/10.1145/1390156.1390208

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

L1-AND L2-LOSS FUNCTION
ACCURATE SOLUTION
DESCENT IMPLEMENTATION
LARGE-SCALE LINEAR SVM
LINEAR SVM
HUGE NUMBER
LARGE-SCALE SPARSE DATA
VECTOR MACHINES
DESCENT METHOD
LINEAR SUPPORT
COORDINATE DESCENT
SPARSE DATA
LOSS FUNCTION
SUPPORT VECTOR MACHINE

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