Efficient Large Scale Linear Programming Support Vector Machines

Sra, S

doi:10.1007/11871842_78

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Efficient Large Scale Linear Programming Support Vector Machines

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://link.springer.com/content/pdf/10.1007%2F11871842_78.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Sra, S. (2006). Efficient Large Scale Linear Programming Support Vector Machines. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 (pp. 767-774). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D041-5

Abstract

This paper presents a decomposition method for efficiently constructing ℓ1-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 setsour method is seen to be not only very fast, but also highly competitive against the leading SVM implementations.