Research map / Learning theory and algorithms / Prediction and decision losses
Active Nearest-Neighbor Learning in Metric Spaces
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace passive compression set construction (removing obstructing points, then t-net) with MARMANN's active label-efficient compression set via majority-vote labeling on a t/2-net | nearest-neighbor classifier in general metric spaces · theoretical guarantee (Theorem 3.2) | label complexity | Helped |
| Generalized compression analysis allowing compression-set examples to take labels different from original labels | nearest-neighbor compression bound · theoretical bound (Theorem 4.1) | generalization bound | No clear effect |
| Active model selection via binary-search over scales with label-bounded error estimation (SelectScale) instead of cross-validation over all scales | MARMANN scale selection · theoretical guarantee (Theorem 5.4, 5.5) | label complexity and generalization bound | Helped |
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