Linganisha mbinu

	Kujifunza kwa Njia ya Kujitolea kwa Kutumia Wanajamaa-K	Ujifundishaji wa Nusu-Nusu wa Majirani-K-Karibu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1951–2010	2002 (semi-supervised extension); 1967 (KNN base)
Mwanzilishi	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)
Aina	Active learning with KNN base learner	Semi-supervised classifier / label propagation
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	4	4
Muhtasari	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.