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Making Large-Scale Support Vector Machine Learning Practical
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, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVMlight is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVMlight V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.
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Metadata
- Authors
- Joachims, T.
- Venue
- Advances in Kernel Methods
- Publisher
- The MIT Press
- Published
- 1998-12-01
- OpenAlex
- W1574862351
- Cited by
- 1,613
- References
- 0
- FWCI
- 32.66
- Type
- book chapter
- Language
- en
- Open access
- closed
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