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,