قارن الطرق

	SVM أحادي الفئة ذاتي الإشراف	آلة المتجهات الداعمة أحادية الفئة شبه المُشرف عليها
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2001–2004
صاحب الطريقة	Golan & El-Yaniv; Ruff et al.	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010
النوع	Self-supervised anomaly/novelty detection	Semi-supervised anomaly / novelty detection
المصدر التأسيسي	Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.
الأسماء البديلة	SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM
ذات صلة	6	5
الملخص	Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.	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.