Making large-scale SVM learning practical

Thorsten Joachims

No 1998,28, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: Training a support vector machine SVM leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples on the shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.

Date: 1998
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:199828

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