Módszerek összehasonlítása

	Robuszt Egyosztályú SVM	Isolation Forest
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2000s–2010s	2008
Megalkotó	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Típus	Anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
Alapmű	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Alternatív nevek	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Kapcsolódó	5	5
Összefoglaló	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.	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.