Sammenlign metoder

	Online Transfer Learning	Semiveiledet læring
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	2010	1970s–2006 (formalized)
Opphavsperson	Zhao, P. & Hoi, S. C. H.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Online learning with source-domain knowledge transfer	Learning paradigm
Opprinnelig kilde	Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	OTL, streaming transfer learning, incremental transfer learning, online domain adaptation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Relaterte	4	5
Sammendrag	Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront.	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.