Research map / Learning theory and algorithms / Prediction and decision losses
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Two-round active sampling: in round two, sample only from the estimated hard region (rejection sampling) versus passive sampling of all points | k-NN classifier (modified k-NN with rejection-sampled second round) · theoretical analysis under beta-margin condition and smoothness | bound on disagreement with Bayes classifier (Pr(g* != g_hat)) | Helped |
| Consistency guarantee for the active scheme without any smoothness assumption on eta, provided x != X_(kbar+1)(x) for first-round sample | active k-NN scheme · theory: convergence to Bayes risk as m grows | R(g_hat) - R* convergence in probability | Helped |
| Modify VC-dimension/tie-breaking: augmented balls in X x [0,1] for l_infinity space give VC bound 2d | augmented ball class · theory: VC dimension of augmented balls | VC dimension bound | No clear effect |
Closest papers by meaning
- An adaptive nearest neighbor rule for classification (NeurIPS 2019 · similarity 0.88)
- k*-Nearest Neighbors: From Global to Local (NeurIPS 2016 · similarity 0.87)
- Active Nearest-Neighbor Learning in Metric Spaces (NeurIPS 2016 · similarity 0.87)
- Statistical Guarantees of Distributed Nearest Neighbor Classification (NeurIPS 2020 · similarity 0.87)
- On Convergence of Nearest Neighbor Classifiers over Feature Transformations (NeurIPS 2020 · similarity 0.86)
- On the Convergence of Loss and Uncertainty-based Active Learning Algorithms (NeurIPS 2024 · similarity 0.82)
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