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	Online aktivní učení	Online Learning
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2000s	1958–2000s
Tvůrce	Cesa-Bianchi, N. and others (multiple contributors)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Typ	Hybrid learning paradigm (online + active)	Learning paradigm (sequential model update)
Původní zdroj	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.
Další názvy	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	incremental learning, sequential learning, streaming learning, online machine learning
Příbuzné	6	6
Shrnutí	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.