Compara mètodes

	Aprenentatge robust de mètriques	Support Vector Machine Robust
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2009–2012	2006–2009
Autor original	Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012)	Xu, H., Caramanis, C., & Mannor, S.
Tipus	Supervised/semi-supervised distance metric learning with robustness to noise and outliers	Robust supervised classifier / regressor
Font seminal	Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036.	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.
Àlies	robust distance metric learning, noise-robust metric learning, outlier-robust similarity learning, robust DML	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM
Relacionats	5	5
Resum	Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks.	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.