Сравнение методов

	Объяснимый одноклассовый SVM	Одноклассовая SVM
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1999 (OCSVM); 2017–present (explainability integration)	1999–2001
Автор метода	Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Anomaly/novelty detection with post-hoc or intrinsic explainability	Anomaly / novelty detection (unsupervised)
Основополагающий источник	Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 12, 582–588.	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	XOC-SVM, Interpretable One-Class SVM, SHAP-augmented OCSVM, Explainable Novelty Detection SVM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	4	3
Сводка	Explainable One-Class SVM pairs the classic One-Class Support Vector Machine anomaly detector — which learns a tight boundary around normal data without requiring labeled anomalies — with post-hoc explainability methods such as SHAP or LIME to reveal which features drive each novelty or anomaly score, converting an opaque decision boundary into an auditable, feature-attributable signal.	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.