Compară metode

	K-Nearest Neighbors Online	K-Nearest Neighbors semi-supervizat
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2010s (formalized in streaming-learning literature)	2002 (semi-supervised extension); 1967 (KNN base)
Autorul original	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)
Tip	Instance-based online classifier/regressor	Semi-supervised classifier / label propagation
Sursa seminală	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.	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Denumiri alternative	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN
Înrudite	5	4
Rezumat	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.