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.
Keywords
Support vector machineComputer scienceQuadratic programmingRanking SVMMachine learningConstraint (computer-aided design)Artificial intelligenceQuadratic equationScale (ratio)Mathematical optimizationMathematics
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Publication Info
- Year
- 2006
- Type
- article
- Citations
- 4316
- Access
- Closed
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Cite This
Thorsten Joachims
(2006).
Making Large-Scale SVM Learning Practical.
Technical reports
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https://doi.org/10.17877/de290r-14262
Identifiers
- DOI
- 10.17877/de290r-14262