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主动学习 K-近邻

主动学习 K-近邻（Active Learning K-Nearest Neighbors）结合了 K-近邻（KNN）的实例式预测与一种迭代式查询策略，该策略选择信息量最大的未标记样本进行标注。模型仅请求那些邻域投票边际最窄的实例的标签，从而以远少于全监督 KNN 的标记样本在表格数据上达到具有竞争力的准确率。

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Method map

The neighbourhood of related methods — select a node to explore.

主动学习 K-近邻
主动学习主动学习决策树主动学习逻辑回归半监督K近邻

来源

  1. Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison. link ↗
  2. Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data, 58–65. link ↗

如何引用本页

ScholarGate. (2026, June 3). Active Learning with K-Nearest Neighbors Classifier. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-k-nearest-neighbors

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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ScholarGate — Active learning K-nearest neighbors (Active Learning with K-Nearest Neighbors Classifier). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/active-learning-k-nearest-neighbors · 数据集： https://doi.org/10.5281/zenodo.20539026