Efficient Large Scale Linear Programming Support Vector Machines

  • Suvrit Sra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)


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.


Support Vector Machine Decomposition Method Training Point Decomposition Procedure Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Suvrit Sra
    • 1
  1. 1.Dept. of Comp. SciencesThe University of Texas at AustinAustinUSA

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