Universum Learning for Multiclass SVM

Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah

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.

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