So sánh phương pháp

	Robust Boosting	Gradient Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1999–2001	2001
Người khởi xướng	Freund, Y.; Mason, L. et al.	Friedman, J. H.
Loại	Ensemble (robust sequential boosting)	Ensemble (sequential boosting of decision trees)
Công trình gốc	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Tên gọi khác	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Liên quan	6	5
Tóm tắt	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.