I'm currently learning the maximal margin classifier with kernels, and I'm wondering - why do they work? In which cases do they work best?I'm especially interested in the RBF and polynomial kernels, but not only.
Why do some known kernel functions manage to achieve linear separation in feature space?
support vector machines