Comparer des méthodes

	Apprentissage actif par ensemble de boosting	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1998	1990–1997
Auteur d'origine	Abe, N. & Mamitsuka, H.	Schapire, R. E.; Freund, Y.
Type	Hybrid active-learning ensemble	Sequential ensemble (iterative reweighting)
Source fondatrice	Abe, N. & Mamitsuka, H. (1998). Query Learning Strategies Using Boosting and Bagging. Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 1–9. Morgan Kaufmann.	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	boosting-based active learning, query learning with boosting, active boosting, ensemble active learning	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	4	6
Résumé	Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — and retrains after each new label, achieving high accuracy with far fewer labeled examples than passive learning.	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.