方法对比

	在线主动学习	主动学习
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000s	2009
提出者	Cesa-Bianchi, N. and others (multiple contributors)	Burr Settles
类型	Hybrid learning paradigm (online + active)	Interactive supervised learning framework
开创性文献	Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
别名	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
相关	6	2
摘要	Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.	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.