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Making Large-Scale SVM Learning Practical (1998)

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  • [www-ai.cs.uni-dortmund.de]
  • [www.joachims.org]
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  • [www.cs.cornell.edu]
  • [www-ai.informatik.uni-dortmund.de]
  • [www-ai.informatik.uni-dortmund.de]
  • [www-ai.cs.uni-dortmund.de]

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by Thorsten Joachims
Citations:1860 - 17 self
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BibTeX

@MISC{Joachims98makinglarge-scale,
    author = {Thorsten Joachims},
    title = {Making Large-Scale SVM Learning Practical},
    year = {1998}
}

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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. SV M light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SV M light V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.

Keyphrases

large-scale svm learning practical    svm learner    support vector machine    linear equality constraint    large task    computational result    quadratic optimization problem    o-the-shelf optimization technique    large domain    time requirement    large-scale svm training    sv light1    many issue    general quadratic program    many training example    bound constraint   

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