Módszerek összehasonlítása

	Aktív Tanulás K-Legközelebbi Szomszédok	Logisztikus regresszióval végzett aktív tanulás
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1951–2010	1994–2010
Megalkotó	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)
Típus	Active learning with KNN base learner	Active learning framework with logistic regression base learner
Alapmű	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.
Alternatív nevek	AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier
Kapcsolódó	4	4
Összefoglaló	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 with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.