Compară metode

	Împachetare Robustă (Robust Bagging)	Boosting Robust (Robust Boosting)
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996–2000s	1999–2001
Autorul original	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Freund, Y.; Mason, L. et al.
Tip	Ensemble (robust bootstrap aggregating)	Ensemble (robust sequential boosting)
Sursa seminală	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.
Denumiri alternative	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
Înrudite	6	6
Rezumat	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.	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.