Multiclass Universum SVM
This work addresses multiclass classification problems, offering a domain-specific improvement with incremental novelty.
The authors tackled the problem of multiclass classification by introducing Universum learning and proposing a novel multiclass universum SVM (MU-SVM) formulation, achieving over 20% improvement in test accuracies compared to multi-class SVM on real-world datasets.
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.