Support Vector Machines: Kernels

Explain how the use of a kernel k (a,b) enables Support Vector Machines to correctly classify all training samples, regardless of whether they are linearly separable in their input space. How is the kernel defined in terms of feature vectors, and which properties of the mapping from input to feature vectors are essential?