Machine learningMachine learning
One-Class SVM
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- 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. DOI: 10.1162/089976601750264965 ↗
- Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45–66. DOI: 10.1023/B:MACH.0000008084.60811.49 ↗
Related methods
Referenced by
Active learning Isolation forestActive learning One-class SVMAutoencoder Anomaly DetectionBayesian Autoencoder Anomaly DetectionBayesian one-class SVMEnsemble Autoencoder Anomaly DetectionEnsemble Isolation ForestEnsemble One-class SVMExplainable Autoencoder Anomaly DetectionExplainable Isolation ForestExplainable One-Class SVMLocal Outlier FactorOnline Autoencoder Anomaly DetectionOnline Isolation ForestOnline One-class SVMRegularized Gaussian Mixture ModelRobust Autoencoder anomaly detectionRobust Gaussian Mixture ModelRobust Isolation forestRobust One-class SVMRobust Support Vector MachineSelf-supervised Autoencoder Anomaly DetectionSelf-supervised Isolation ForestSelf-supervised One-class SVMSemi-supervised Autoencoder Anomaly DetectionSemi-supervised Isolation ForestSemi-supervised One-class SVM