Comparer des méthodes

	Boosting	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	1970s–2006 (formalized)
Auteur d'origine	Schapire, R. E.; Freund, Y.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Sequential ensemble (iterative reweighting)	Learning paradigm
Source fondatrice	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	6	5
Résumé	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.