方法证据记录
Regularized k-nearest neighbors
Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.
源记录
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Regularized k-Nearest Neighbors (Kernel-Weighted kNN)
分类方法记录 · ml-model / machine-learning
- Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. · DOI 10.1109/TIT.1967.1053964
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 13). Springer. · ISBN 978-0-387-84858-7
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