Comparer des méthodes

	Machine à vecteurs de support robuste	Machine à vecteurs de support régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006–2009	1995–2004
Auteur d'origine	Xu, H., Caramanis, C., & Mannor, S.	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Type	Robust supervised classifier / regressor	Regularized discriminative classifier / regressor
Source fondatrice	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Alias	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
Apparentées	5	4
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.	Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.