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Making Large-Scale Support Vector Machine Learning Practical (1998)
| Citations: | 628 - 1 self |
BibTeX
@MISC{Joachims98makinglarge-scale,
author = {Thorsten Joachims},
title = { Making Large-Scale Support Vector Machine 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. SVM light1 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 V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.
Keyphrases
svm learner support vector machine linear equality constraint large task computational result quadratic optimization problem general quadratic program time requirement off-the-shelf optimization technique bound constraint large-scale svm training sv light1 many issue large domain many training example
