Research map / Learning theory and algorithms / Submodular and game algorithms
Budgeted stream-based active learning via adaptive submodular maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Selecting the largest expected marginal gain item per segment (AdaptiveStream) instead of uncertainty sampling within segments | stream-based active learning · WDBC and MNIST, budgets k=30,40,50 | error rate | Helped |
| Selecting the largest expected marginal gain item per segment (AdaptiveSecretary via classical secretary rule) instead of uncertainty sampling within segments | secretary-based active learning · WDBC and MNIST, budgets k=30,40,50 | error rate | Helped |
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