Comparer des méthodes

	Machine à vecteurs de support robuste	Gradient Boosting Robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006–2009	2001
Auteur d'origine	Xu, H., Caramanis, C., & Mannor, S.	Friedman, J. H. (with Huber loss from Huber, P. J.)
Type	Robust supervised classifier / regressor	Ensemble (boosted trees with robust loss)
Source fondatrice	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Alias	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Apparentées	5	6
Résumé	Robust SVM extends the standard support vector machine to resist the influence of outliers and mislabeled points. By replacing the hinge loss with a bounded or non-convex loss function — or by incorporating robust optimization constraints — it learns a decision boundary that is far less distorted by corrupted training examples, making it suitable for noisy real-world datasets where standard SVM would degrade significantly.	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.