Сравнение на методи

	Самообучаващ се Isolation Forest	Еднокласов SVM
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2008–2020s	1999–2001
Създател	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Ensemble anomaly detector with self-supervised pre-training	Anomaly / novelty detection (unsupervised)
Основополагащ източник	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	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.
Други названия	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Свързани	4	3
Резюме	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	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.