I want to train SVMs in R and I know there are functions such as e1071::tune.svm() that can be used to find the optimal parameters for the SVM. However, it seems there are some formulas out there (e.g. used in this report) that can give you a reasonable estimate of these parameters.
Since a grid-search for the parameters can take quite a lot of time on larger datasets and usually, one has to provide a range of possible values anyway, I wondered whether there is a package that implements formulas to get a quick estimate for the gamma and cost parameters for the SVM?
So far, I've found out that caret::train() might use such an approach to estimate sigma (which should be the reciprocal of 2*gamma^2) but I haven't tried it yet, since other calculations are still running (and will be, probably for the next days). Is there also an implementation to estimate cost or at least give a range of reasonable values?
I have found a similar question that asks for alternatives to grid-search in general. However, I would be interested in an R implementation of such alternatives and also, I hope things have developed further since the more general question was posted years ago.