قارن الطرق

	آلة المتجهات الداعمة القوية	آلة المتجهات الداعمة أحادية الفئة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2006–2009	1999–2001
صاحب الطريقة	Xu, H., Caramanis, C., & Mannor, S.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
النوع	Robust supervised classifier / regressor	Anomaly / novelty detection (unsupervised)
المصدر التأسيسي	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
الأسماء البديلة	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
ذات صلة	5	3
الملخص	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.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.