Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2018 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1808.08111 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sauptik Dhar | 1 | 48 | 5.75 |
| Vladimir Cherkassky | 2 | 1064 | 126.66 |
| mohak shah | 3 | 36 | 8.78 |