方法对比

	在线主动学习	在线学习
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000s	1958–2000s
提出者	Cesa-Bianchi, N. and others (multiple contributors)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
类型	Hybrid learning paradigm (online + active)	Learning paradigm (sequential model update)
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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
别名	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	incremental learning, sequential learning, streaming learning, online machine learning
相关	6	6
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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.