Parameter selection of SVM

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Sepp
Sepp 2015-6-18
Hi
I have a dataset which I use for classifcation with libSVM in Matlab. The dataset consists of 4 classes.
For parameter selection of SVM I can do nested cross-validation. The problem is that I also need the value of the best parameters in the end.
After having done the nested cross-validation and having the final accuracy I want the values of the best parameters. Then I will train a SVM for each class (one-vs-all) with the best parameters for selecting the most important features (according to heighest weight), i.e. feature importance map.
How can I do this? Should I just not do nested cross-validation and only looping over all parameters and doing cross-validation?
Second, if I use a linear SVM then using this weight vector w for assigning importance to features works, but does it also work for non-linear SVM (e.g. rbf kernel)?

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