قارن الطرق

	SVM أحادي الفئة ذاتي الإشراف	غابة العزل
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2008
صاحب الطريقة	Golan & El-Yaniv; Ruff et al.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
النوع	Self-supervised anomaly/novelty detection	Unsupervised ensemble (random partitioning trees)
المصدر التأسيسي	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.
الأسماء البديلة	SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ذات صلة	6	5
الملخص	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.