Comparar métodos

	Máquina de Vectores de Soporte Robusta	Máquina de Vectores de Soporte Regularizada
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2006–2009	1995–2004
Autor original	Xu, H., Caramanis, C., & Mannor, S.	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Tipo	Robust supervised classifier / regressor	Regularized discriminative classifier / regressor
Fuente seminal	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
Relacionados	5	4
Resumen	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.