Сравнение методов

	Одноклассовая SVM с частичной разметкой	Одноклассовая SVM
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2001–2004	1999–2001
Автор метода	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Semi-supervised anomaly / novelty detection	Anomaly / novelty detection (unsupervised)
Основополагающий источник	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.	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.
Другие названия	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	5	3
Сводка	Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.	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.