Comparar métodos

	Máquina de Vetores de Suporte Robusta	Gradient Boosting Robusto
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2006–2009	2001
Autor original	Xu, H., Caramanis, C., & Mannor, S.	Friedman, J. H. (with Huber loss from Huber, P. J.)
Tipo	Robust supervised classifier / regressor	Ensemble (boosted trees with robust loss)
Fonte seminal	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Outros nomes	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Relacionados	5	6
Resumo	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.	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.