Comparer des méthodes

	Modèle de mélange gaussien régularisé	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	1999–2001
Auteur d'origine	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Probabilistic clustering with regularization	Anomaly / novelty detection (unsupervised)
Source fondatrice	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	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.
Alias	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	5	3
Résumé	A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.	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.