方法对比

	主动学习 K-近邻	主动学习逻辑回归
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1951–2010	1994–2010
提出者	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)
类型	Active learning with KNN base learner	Active learning framework with logistic regression base learner
开创性文献	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.
别名	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
相关	4	4
摘要	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.