Comparer des méthodes

	Bagging (Bootstrap Aggregating)	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1970s–2006 (formalized)
Auteur d'origine	Breiman, L.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Learning paradigm
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	5	5
Résumé	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.