Multi-class classification is an important and on-going research
subject in machine learning. Current support vector methods for multi-class
classification implicitly assume that the parameters in the optimization
problems to be known exactly. However, in practice, the parameters have
perturbations since they are estimated from the training data which are usually
subject to measurement noise. In this paper, we propose linear and nonlinear
robust formulations for multi-class classification based on M-SVM method.
The preliminary numerical experiments concerm the robustness of the proposed
method.