Methoden vergelijken

	Online One-Class SVM	Isolation Forest
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2006 (incremental/online variant); 1999 (base method)	2008
Grondlegger	Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Type	Online anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
Oorspronkelijke bron	Laskov, P., Gehl, C., Krueger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Aliassen	Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Verwant	4	5
Samenvatting	Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.	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.