Machine learningMachine learning
在线K近邻
在线K近邻（Online KNN）将经典的KNN算法适配于数据流场景，其中观测值按顺序到达，模型必须在不进行完全重新训练的情况下进行增量更新。它不存储所有历史实例，而是维护一个有限的滑动窗口或自适应内存，通过邻近度来分类或预测每个传入点，使用最近且最具代表性的样本。
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来源
- Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE. DOI: 10.1109/ICDM.2016.0040 ↗
- Gama, J. (2010). Knowledge Discovery from Data Streams. CRC Press / Chapman & Hall. ISBN: 978-1-4398-2611-9
如何引用本页
ScholarGate. (2026, June 3). Online K-Nearest Neighbors (Incremental KNN for Data Streams). ScholarGate. https://scholargate.app/zh/machine-learning/online-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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