Salīdzināt metodes

	Aktīvās apmācības viena klases SVM	Vienas klases SVM
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s	1999–2001
Autors	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.
Tips	Semi-supervised anomaly/novelty detection with iterative labeling	Anomaly / novelty detection (unsupervised)
Pirmavots	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.
Citi nosaukumi	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Saistītās	4	3
Kopsavilkums	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.