Sciweavers

Share
ECML
2006
Springer

Efficient Large Scale Linear Programming Support Vector Machines

8 years 6 months ago
Efficient Large Scale Linear Programming Support Vector Machines
This paper presents a decomposition method for efficiently constructing 1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many desirable properties. For example, it is provably convergent, scales well to large datasets, is easy to implement, and can be extended to handle support vector regression and other SVM variants. We demonstrate the efficiency of our algorithm by training on (dense) synthetic datasets of sizes up to 20 million points (in R32 ). The results show our algorithm to be several orders of magnitude faster than a previously published method for the same task. We also present experimental results on real data sets--our method is seen to be not only very fast, but also highly competitive against the leading SVM implementations.
Suvrit Sra
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where ECML
Authors Suvrit Sra
Comments (0)
books