Choosing multiple parameters for support vector machines

Choosing multiple parameters for support vector machines (2002)

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by Olivier Chapelle , École Polytechnique , Nello Cristianini

Venue:	Machine Learning
Citations:	210 - 15 self

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@INPROCEEDINGS{Chapelle02choosingmultiple,
    author = {Olivier Chapelle and École Polytechnique and Nello Cristianini},
    title = {Choosing multiple parameters for support vector machines},
    booktitle = {Machine Learning},
    year = {2002},
    pages = {2002}
}

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Abstract

Abstract. The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

Citations

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