Im trying to learn some hyper-parameters for SVM classifier,

I want to know if there is any correlation between the kernel parameters and the regularization parameter – C,. because if not i can then try optimizing the C parameter and only when one has being optimized start with the kernel parameter, which will save me alot of runtime.

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#### Best Answer

In principle, no. One cannot optimize one parameter and then the other.

There is (at least) one paper that proposes a method to optimize first the C (using a Linear SVM) and then the gamma.

http://www.mitpressjournals.org/doi/abs/10.1162/089976603321891855#.WE3VlpJrWLA

but I tried this and it did not work well on many datasets. Two problems (a) the selection it makes is not that great and (b) it takes a surprising long time – because the linear SVM is not that fast (I did not use the LibLinear implementation – I used libSVM with the linear kernel).