方法对比
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| 在线K近邻× | 半监督K近邻× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s (formalized in streaming-learning literature) | 2002 (semi-supervised extension); 1967 (KNN base) |
| 提出者≠ | Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016) | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) |
| 类型≠ | Instance-based online classifier/regressor | Semi-supervised classifier / label propagation |
| 开创性文献≠ | 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 ↗ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 别名 | Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN |
| 相关≠ | 5 | 4 |
| 摘要≠ | Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity. | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. |
| ScholarGate数据集 ↗ |
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