  We introduce Universum learning for multiclass problems and propose a novel
formulation for multiclass universum SVM (MU-SVM). We also propose an analytic
span bound for model selection with almost 2-4x faster computation times than
standard resampling techniques. We empirically demonstrate the efficacy of the
proposed MUSVM formulation on several real world datasets achieving > 20%
improvement in test accuracies compared to multi-class SVM.
