Comparer des méthodes

	Boosting	Robust Bagging
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	1996–2000s
Auteur d'origine	Schapire, R. E.; Freund, Y.	Breiman, L. (bagging); robust variants developed by various authors in 2000s
Type	Sequential ensemble (iterative reweighting)	Ensemble (robust bootstrap aggregating)
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.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing
Apparentées	6	6
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.	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.