Порівняння методів

	Активне навчання з K-найближчими сусідами	Напівавтоматичний метод K-найближчих сусідів
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1951–2010	2002 (semi-supervised extension); 1967 (KNN base)
Автор методу	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)
Тип	Active learning with KNN base learner	Semi-supervised classifier / label propagation
Основоположне джерело	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.
Інші назви	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
Пов'язані	4	4
Підсумок	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.