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Choosing multiple parameters for support vector machines
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DOI：10.1023/A:1012450327387.png)
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Abstract
En 中文
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.
Keywords:
support vector machines
kernel selection
leave-one-out procedure
gradient descent
feature selection
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