Сравнение методов

	Полуавтоматическое обнаружение аномалий с помощью автоэнкодера	Одноклассовая SVM
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2018–2020	1999–2001
Автор метода	Ruff, L. et al.; Zong, B. et al.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Semi-supervised deep anomaly detection	Anomaly / novelty detection (unsupervised)
Основополагающий источник	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).	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	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	5	3
Сводка	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.	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.