So sánh phương pháp

	Bagging Mạnh mẽ (Robust Bagging)	Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996–2000s	1990–1997
Người khởi xướng	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Schapire, R. E.; Freund, Y.
Loại	Ensemble (robust bootstrap aggregating)	Sequential ensemble (iterative reweighting)
Công trình gốc	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	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	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Liên quan	6	6
Tóm tắt	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.	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.