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Making Large-Scale SVM Learning Practical (1998)
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@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
