Comparer des méthodes

	Apprentissage Actif Robuste	Machine à vecteurs de support robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006	2006–2009
Auteur d'origine	Balcan, M.-F.; Beygelzimer, A.; Langford, J.	Xu, H., Caramanis, C., & Mannor, S.
Type	Active learning with robustness guarantees	Robust supervised classifier / regressor
Source fondatrice	Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM.	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.
Alias	RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM
Apparentées	6	5
Résumé	Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process.	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.