# About SVM cost and gamma parameters tuning

I am using R and e1071 package to tune a C-classification SVM.

My question is: regardless of the kernel type (linear, polynomial, radial basis or sigmoidal), is there any good criterion to choose the range in which cost and $\gamma$ parameters should range over and/or to choose what the granularity should be (that is, as an example, gamma = 10 ^ (1:2) or gamma = 1:2 or gamma = 100 ^ (1:2))?

I add a second question: can tune.svm() return the best kernel type, too?

Thanks,

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Thank you for your suggestment, Dikran Marsupial. Let you have to decide which is the best kernel type and the best value of cost and $\gamma$ parameters: what would you optimize first, according to which criterion and why? – Lisa Ann Nov 21 '12 at 11:06
Is the Kernel Logistic Regression like a non parametric Logit model (or similar to that one)? I will try it, thank you. Could you suggest me any R package with KLR? – Lisa Ann Nov 21 '12 at 12:33