  We introduce Universum learning for multiclass problems and propose a novel
formulation for multiclass universum SVM (MU-SVM). We also propose a span bound
for MU-SVM that can be used for model selection thereby avoiding resampling.
Empirical results demonstrate the effectiveness of MU-SVM and the proposed
bound.
