Equation ( ) presents the optimisation problem that is solved when training the Kreȋn-SVM with L2 loss. We denote by K the associated RKKS. Similarly to the SVM, the solution is a function f ∈ K and a vector of slack variables ξ which minimise the objective function. The first term in the objective function, as well as the constraints, have the same interpretation as in eq. ( ). The only notable difference to the SVM is the method of regularisation. Any RKKS K can be expressed as a direct sum of the form