विधियों की तुलना करें

	सेल्फ-सुपरवाइज्ड वन-क्लास एसवीएम	आइसोलेशन फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2018	2008
प्रवर्तक	Golan & El-Yaniv; Ruff et al.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
प्रकार	Self-supervised anomaly/novelty detection	Unsupervised ensemble (random partitioning trees)
मौलिक स्रोत	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.
उपनाम	SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
संबंधित	6	5
सारांश	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.