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	Solidne zwiększanie (Robust Boosting)	Wzmocnienie
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1999–2001	1990–1997
Twórca	Freund, Y.; Mason, L. et al.	Schapire, R. E.; Freund, Y.
Typ	Ensemble (robust sequential boosting)	Sequential ensemble (iterative reweighting)
Źródło pierwotne	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	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.
Inne nazwy	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Pokrewne	6	6
Podsumowanie	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.	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.