Comparer des méthodes

	Apprentissage actif par ensemble d'empilement	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992–2012	1990–1997
Auteur d'origine	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Schapire, R. E.; Freund, Y.
Type	Hybrid (active learning + stacked ensemble)	Sequential ensemble (iterative reweighting)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	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.
Alias	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	5	6
Résumé	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	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.