Bandingkan metode

	Pembelajaran Aktif K-Nearest Neighbors	K-Nearest Neighbors Semi-Terawasi
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1951–2010	2002 (semi-supervised extension); 1967 (KNN base)
Pencetus	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)
Tipe	Active learning with KNN base learner	Semi-supervised classifier / label propagation
Sumber perintis	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN
Terkait	4	4
Ringkasan	Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data.	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.