Сравнение методов

	Robust Bagging	Robust Boosting
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996–2000s	1999–2001
Автор метода	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Freund, Y.; Mason, L. et al.
Тип	Ensemble (robust bootstrap aggregating)	Ensemble (robust sequential boosting)
Основополагающий источник	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.
Другие названия	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
Связанные	6	6
Сводка	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.