Neural Information Processing Systems http://nips.cc/

We introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM) by deriving the Natarajan dimension for sample complexity for PAC-learnability of MU-SVM. We also propose an analytic span bound for MU-SVM and demonstrate its utility for model selection resulting in $\sim 2-4 \times$ faster computation times than standard resampling techniques. We empirically demonstrate the efficacy of MU-SVM on several real world datasets achieving $> $ 20\% improvement in test accuracies compared to M-SVM. Insights into the underlying behavior of MU-SVM using a histograms-of-projections method are also provided.

We introduce the notion of learning from contradictions, a.k.a

Originality: The proposed method is a novel combination of existing methods. This combination comes with theoretical guarantees and practical tools to deal with the obvious tuning complexity. Quality: All claims and proposition are justified and detailed (if not in the paper, in the supplementary material). I did not find flaws in it. The interest of incorporating universum examples is shown (already done in binary case) and the motivation to adapt the framework to multiclass case, is established: forcing a universum example to be neutral for all classes makes sense.

We introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM) by deriving the Natarajan dimension for sample complexity for PAC-learnability of MU-SVM. We also propose an analytic span bound for MU-SVM and demonstrate its utility for model selection resulting in \sim 2-4 \times faster computation times than standard resampling techniques. We empirically demonstrate the efficacy of MU- SVM on several real world datasets achieving 20\% improvement in test accuracies compared to M-SVM.

We introduce the notion of learning from contradictions, a.k.a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We show that learning from contradictions (using MU-SVM) incurs lower sample complexity compared to multiclass SVM (M-SVM) by deriving the Natarajan dimension for sample complexity for PAC-learnability of MU-SVM. We also propose an analytic span bound for MU-SVM and demonstrate its utility for model selection resulting in $\sim 2-4 \times$ faster computation times than standard resampling techniques. We empirically demonstrate the efficacy of MU- SVM on several real world datasets achieving $ $ 20\% improvement in test accuracies compared to M-SVM.