مقایسهٔ روش‌ها

	یک کلاسه SVM نیمه‌نظارت‌شده	ماشین بردار پشتیبان تک کلاسه
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2001–2004	1999–2001
پدیدآور	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
نوع	Semi-supervised anomaly / novelty detection	Anomaly / novelty detection (unsupervised)
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	5	3
خلاصه	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.