قارن الطرق

	التعلم النشط لآلة المتجهات الداعمة أحادية الفئة	آلة المتجهات الداعمة أحادية الفئة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2000s	1999–2001
صاحب الطريقة	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
النوع	Semi-supervised anomaly/novelty detection with iterative labeling	Anomaly / novelty detection (unsupervised)
المصدر التأسيسي	Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471.	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	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
ذات صلة	4	3
الملخص	Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort.	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.