Сравнение методов

	Байесовская одноклассовая SVM	Одноклассовая SVM
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2001–2010	1999–2001
Автор метода	Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Probabilistic anomaly detection	Anomaly / novelty detection (unsupervised)
Основополагающий источник	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.	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	Bayesian OCSVM, Bayesian one-class classifier, probabilistic one-class SVM, Bayes-OCSVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	6	3
Сводка	Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decision, making the approach more suitable when downstream actions depend on how confident the model is that a new observation is anomalous.	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.