I want to find the best choice of $C$ and $\gamma$ parameters for Radial Basis Function kernel.
I am using kernlab instead of e1071 library. So how can i optimize RBF parameters $C$ and $\gamma$ with ksvm function?
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$\begingroup$ This question is just a more specific version of this one. stats.stackexchange.com/questions/193306/… $\endgroup$– Sycorax ♦Apr 11, 2018 at 16:03
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You can use Optunity for that purpose. This is a library of optimization algorithms for automated hyperparameter search.
You can find an example of using Optunity to optimize an SVM in e1707 here, mapping this to kernlab should be straightforward. All you need to do is specify box constraints (a lower and upper bound) on $C$ and $\gamma$ and a budget of function evaluation (i.e., how many ($C$, $\gamma$)-pairs can be tested.
answered Jul 11, 2015 at 14:12
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1$\begingroup$ I use R for svm classification, but i can not install Optiunity package. Thankyou Mr. Claesen $\endgroup$ Jul 13, 2015 at 5:04
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1$\begingroup$ Excuse me Mr. Claesn, I didn't know that you are main contributor of Optunity package. At last, the command
install_github("claesenm/optunity/wrappers/R")worked for me! $\endgroup$ Jul 13, 2015 at 6:22 -
$\begingroup$ Glad to hear it's working! If you run into any problems, please let us know via Github issues so we can solve them ASAP. $\endgroup$ Jul 13, 2015 at 6:29