A Dual Coordinate Descent Method for Large-scale Linear SVM
author: Kai-Wei Chang,
Department of Computer Science and Information Engineering, National Taiwan University
published: Aug. 5, 2008, recorded: July 2008, views: 6614
published: Aug. 5, 2008, recorded: July 2008, views: 6614
Slides
Related content
Report a problem or upload files
If 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.
Description
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.
Download slides:
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Reviews and comments:
Recently there has been a lot of interest in streamlining multi-label training tasks by proposing faster methods that converge to the optimal solution with a much fewer number of iterations compared to existing methods. Most students feel good to visit https://www.assignmentholic.co.uk/our... website to complete their essays. A major contribution was made by Graves et al. using submodular flows. Their algorithm uses a submodular function optimization approach which reduces the time complexity drastically as compared to greedy alternatives used.
The article's utilization of http://chess-online.co/ pertinent examples and case studies struck me as instructive and illuminating.
Write your own review or comment: