Comparer des méthodes

	Apprentissage en ligne semi-supervisé	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	1970s–2006 (formalized)
Auteur d'origine	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Hybrid learning paradigm (online + semi-supervised)	Learning paradigm
Source fondatrice	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	4	5
Résumé	Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.	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.