Linganisha mbinu

	Kujifunza kwa Njia ya Kujitolea kwa Kutumia Wanajamaa-K	Active Learning Logistic Regression
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1951–2010	1994–2010
Mwanzilishi	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)
Aina	Active learning with KNN base learner	Active learning framework with logistic regression base learner
Chanzo asilia	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.
Majina mbadala	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
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.	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.