We denote by x i the i-th training instance and by y i its corresponding label. Furthermore, H denotes the RKHS from which a solution is found. The solution is a function f ∈ H and a vector of slack variables ξ which minimise the objective function, subject to the constraints. The constraint y i f (x i ) ≥ 1ξ i imposes that the i-th instance lies on the correct side of the hyperplane and that its distance to the hyperplane is greater than or equal to the margin. An instance x j which does not satisfy this constraint contributes a value of ξ 2 j to the objective function, where ξ j is the distance between x j and the margin. The ∑ n i=1 ξ 2 i term in the objective function is the total contribution of all instances which do not satisfy the constraints. The ∥ f ∥ 2 H term is the norm of the considered function and acts as a regulariser. The hyperparameter λ can be tuned to provide a balance between the number of instances that violate the constraints and the regulariser.