Research map / Label-efficient and robust learning / Recognition and long-tail
Multi-Class Optimal Margin Distribution Machine
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replacing multi-class SVM margin optimization with margin-distribution optimization (mcODM) | multi-class classification · 22 benchmark datasets (Table 2) | accuracy / average rank: mcODM average rank 1.09 vs 2.45 for best baseline mcSVM; significantly better than mcSVM on 17 of 22 datasets | Helped |
| Using a sorting-based solver for the sub-problem instead of general QP solvers | mcODM · single iteration time on large datasets (Figure 3) | CPU time | No clear effect |
| Increasing number of classes from four to seven on segment | baseline multi-class SVMs (mcSVM, ovaSVM, ovoSVM, ecocSVM) · segment, generalization performance | generalization performance | Hurt |
| Increasing number of classes on segment, mcODM keeps margin distribution sharper and stable | mcODM · segment, margin distribution and generalization | generalization performance | Helped |
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