How to figure out optimal C / Gamma parameters in libsvm?

Posted by Cuga on Stack Overflow See other posts from Stack Overflow or by Cuga
Published on 2010-05-03T21:11:28Z Indexed on 2010/05/03 21:18 UTC
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libsvm

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svm

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machine-learning

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java

I'm using libsvm for multi-class classification of datasets with a large number of features/attributes (around 5,800 per each item). I'd like to choose better parameters for C and Gamma than the defaults I am currently using.

I've already tried running easy.py, but for the datasets I'm using, the estimated time is near forever (ran easy.py at 20, 50, 100, and 200 data samples and got a super-linear regression which projected my necessary runtime to take years).

Is there a way to more quickly arrive at better C and Gamma values than the defaults? I'm using the Java libraries, if that makes any difference.

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