Research map / Learning theory and algorithms / Prediction and decision losses
An adaptive nearest neighbor rule for classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use AKNN (adaptive k per query) instead of k-NN with the optimal fixed k | AKNN · MNIST with random label noise | accuracy | No clear effect |
| increase label noise level on MNIST for fixed small k | k-NN · MNIST with random label noise | accuracy | Hurt |
| increase confidence parameter A | AKNN · notMNIST | abstention rate | Mixed |
Closest papers by meaning
- Rates of Convergence for Large-scale Nearest Neighbor Classification (NeurIPS 2019 · similarity 0.89)
- k*-Nearest Neighbors: From Global to Local (NeurIPS 2016 · similarity 0.89)
- A Two-Stage Active Learning Algorithm for k-Nearest Neighbors (ICML 2023 · similarity 0.88)
- Nearest Neighbour with Bandit Feedback (NeurIPS 2023 · similarity 0.88)
- On Convergence of Nearest Neighbor Classifiers over Feature Transformations (NeurIPS 2020 · similarity 0.88)
- Weighted distance nearest neighbor condensing (ICML 2024 · similarity 0.85)
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