विधियों की तुलना करें

	अर्ध-पर्यवेक्षित आइसोलेशन फ़ॉरेस्ट	वन-क्लास एसवीएम
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2013–2020	1999–2001
प्रवर्तक	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
प्रकार	Ensemble anomaly detection (semi-supervised extension)	Anomaly / novelty detection (unsupervised)
मौलिक स्रोत	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
उपनाम	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
संबंधित	6	3
सारांश	Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.