Comparer des méthodes

	Processus Gaussien Robuste	Machine à vecteurs de support robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	2006–2009
Auteur d'origine	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Xu, H., Caramanis, C., & Mannor, S.
Type	Probabilistic non-parametric regression / classification	Robust supervised classifier / regressor
Source fondatrice	Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257.	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.
Alias	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM
Apparentées	5	5
Résumé	Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations.	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.