I am using
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?