Compară metode

	Învățare activă online	Învățare semi-supervizată
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2000s	1970s–2006 (formalized)
Autorul original	Cesa-Bianchi, N. and others (multiple contributors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tip	Hybrid learning paradigm (online + active)	Learning paradigm
Sursa seminală	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Denumiri alternative	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Înrudite	6	5
Rezumat	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.