เปรียบเทียบวิธี

	Robust One-Class SVM	Isolation Forest
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2000s–2010s	2008
ผู้ริเริ่ม	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ประเภท	Anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ที่เกี่ยวข้อง	5	5
สรุป	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.