مقایسهٔ روش‌ها

	یادگیری خودنظارتی ماشین بردار تک کلاسه (Self-supervised One-class SVM)	جنگل ایزوله (Isolation Forest)
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2018	2008
پدیدآور	Golan & El-Yaniv; Ruff et al.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
نوع	Self-supervised anomaly/novelty detection	Unsupervised ensemble (random partitioning trees)
منبع بنیادین	Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
نام‌های دیگر	SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
مرتبط	6	5
خلاصه	Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.