Comparer des méthodes

	AdaBoost	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1997	1970s–2006 (formalized)
Auteur d'origine	Freund, Y. & Schapire, R.E.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Ensemble (sequential boosting of weak learners)	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 (Adaptive Boosting), adaptive boosting, adaptif artırma	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	5	5
Résumé	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.