Comparer des méthodes

	Apprentissage ensembliste en ligne	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1970s–2006 (formalized)
Auteur d'origine	Oza, N. C. & Russell, S.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Ensemble (online / incremental)	Learning paradigm
Source fondatrice	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	6	5
Résumé	Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions.	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.