Bandingkan metode

	K-Nearest Neighbors Daring (Online KNN)	Pembelajaran Daring
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2010s (formalized in streaming-learning literature)	1958–2000s
Pencetus	Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tipe	Instance-based online classifier/regressor	Learning paradigm (sequential model update)
Sumber perintis	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	incremental learning, sequential learning, streaming learning, online machine learning
Terkait	5	6
Ringkasan	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.