A dual coordinate descent method for large-scale linear SVM

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 -accurate solution in O(log(1/ )) 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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan
• Keywords: Coordinate Descent Method, ICML 2008, Linear Svm, Machine Learning, Primal Coordinate Descent