Comparer des méthodes

	Machine à vecteurs de support robuste	Régression linéaire robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006–2009	1964–1987
Auteur d'origine	Xu, H., Caramanis, C., & Mannor, S.	Huber, P. J.; Rousseeuw, P. J.
Type	Robust supervised classifier / regressor	Outlier-resistant supervised regression
Source fondatrice	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Alias	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Apparentées	5	5
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 linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.