Сравнение методов

	Робастная одноклассовая SVM (Robust One-Class SVM)	Робастная машина опорных векторов
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s–2010s	2006–2009
Автор метода	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Xu, H., Caramanis, C., & Mannor, S.
Тип	Anomaly detection / novelty detection	Robust supervised classifier / regressor
Основополагающий источник	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.
Другие названия	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM
Связанные	5	5
Сводка	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.	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.