Kernel-Based SVM

Kernel Support Vector Machine is useful to deal with nonlinear classification based on a linear discriminant function in a high-dimensional (kernel) space and employs kernel trick which permits us to work in the input space instead of dealing with a potentially high- dimensional, even theoretically infinite dimensional, kernel (feature) space.