Thread: Libsvm
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Old 05-26-2012, 08:15 AM
alfansome alfansome is offline
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Join Date: Apr 2012
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Default Re: Libsvm

Quote:
Originally Posted by elkka View Post
I currently struggle to understand how to use the build in cross-validation capability. I don't understand yet what exactly I don't understand, but I definitely don't understand something.

Specifically, when using 1-vs-1 classification on the digit set, I get some result for E_in error, that is close to E_out error. But whatever my parameters, the cross-validation accuracy on the problem gives me 99.8% accuracy, which is way higher than E_in or E_out. Any ideas?
Code:
	cva = svmtrain(Y, X, '-t 1 -d 2 -g 1 -r 1 -v 10  -c 0.01' );
I had this situation also; cv error is always the same. I also am saving the models that I generate (using sum_save_model function in the python script) but in looking at them, don't understand the values that show for the data points. Maybe scaling data would help as kkkkk did.

I have now applied data scaling (my own version) and this did result in discrete cva measures for the various C values, although cva does seem high still

Last edited by alfansome; 05-26-2012 at 09:18 AM. Reason: updated
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