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  Efficient Large Scale Linear Programming Support Vector Machines

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.

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Sra, S1, Author           
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 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 sets—our method is seen to be not only very fast, but also highly competitive against the leading SVM implementations.

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 Dates: 2006-09
 Publication Status: Published in print
 Pages: -
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 Identifiers: DOI: 10.1007/11871842_78
BibTex Citekey: 5221
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Title: 17th European Conference on Machine Learning (ECML 2006)
Place of Event: Berlin, Germany
Start-/End Date: 2006-09-18 - 2006-09-22

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Title: Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006
Source Genre: Proceedings
 Creator(s):
Fürnkranz, J, Editor
Scheffer, T, Editor
Spiliopoulou, M, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 767 - 774 Identifier: ISBN: 978-3-540-45375-8