So sánh phương pháp

	Active learning Stacking ensemble	Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1992–2012	1990–1997
Người khởi xướng	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Schapire, R. E.; Freund, Y.
Loại	Hybrid (active learning + stacked ensemble)	Sequential ensemble (iterative reweighting)
Công trình gốc	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.
Tên gọi khác	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Liên quan	5	6
Tóm tắt	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.