Relation to L2 SVM The classic soft-margin SVM formulation uses L1-penalty for misclassified instances.
the objective of the learner is to minimize 1 2 ||w|| 2 + C i ξ i subject to some margin constraints, where w is a weight vector to be learned and ξ i is the misclassification error for instance i.