Linganisha mbinu

	Msitu Tenga Unaojifunza Wenyewe	One-Class SVM
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2008–2020s	1999–2001
Mwanzilishi	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.
Aina	Ensemble anomaly detector with self-supervised pre-training	Anomaly / novelty detection (unsupervised)
Chanzo asilia	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.
Majina mbadala	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Zinazohusiana	4	3
Muhtasari	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.