A Dual Coordinate Descent Method for Large-scale Linear SVM
published: Aug. 5, 2008, recorded: July 2008, views: 6618
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !