방법 비교

	로버스트 서포트 벡터 머신	One-Class SVM
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2006–2009	1999–2001
창시자	Xu, H., Caramanis, C., & Mannor, S.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
유형	Robust supervised classifier / regressor	Anomaly / novelty detection (unsupervised)
원전	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.
별칭	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
관련	5	3
요약	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.