Сравнение на методи

	Робастна еднокласова SVM	Isolation Forest
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2000s–2010s	2008
Създател	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Тип	Anomaly detection / novelty detection	Unsupervised ensemble (random partitioning trees)
Основополагащ източник	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.
Други названия	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Свързани	5	5
Резюме	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.