Uporedite metode

	Aktivno učenje K-najbližih suseda	Aktivno učenje
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	1951–2010	2009
Tvorac	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Burr Settles
Tip	Active learning with KNN base learner	Interactive supervised learning framework
Temeljni izvor	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Drugi nazivi	AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Srodne	4	2
Sažetak	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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.