Sammenlign metoder

	Online Active Learning	Online læring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2000s	1958–2000s
Ophavsperson	Cesa-Bianchi, N. and others (multiple contributors)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Hybrid learning paradigm (online + active)	Learning paradigm (sequential model update)
Oprindelig kilde	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.
Aliasser	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	incremental learning, sequential learning, streaming learning, online machine learning
Relaterede	6	6
Resumé	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.