# Mixed SVM kernel of RBF and linear

I've read some introduction about different kernels for SVM. It seems RBF is a measure of point distance while the basic kernel (i.e. no kernel) splits the space by hyper-planes.

I could imagine that for a mix of features, some features should be treated with RBF and some with the basic kernnel.

Is it possible use RBF for some features and the basic vector product for the other features?

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 I'd look into something called multiple kernel learning. – gamerx Mar 23 at 7:35