Comparer des méthodes

	Modèle robuste de mélange gaussien	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	1999–2001
Auteur d'origine	Peel, D. & McLachlan, G. J.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Probabilistic clustering / density estimation	Anomaly / novelty detection (unsupervised)
Source fondatrice	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	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	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	5	3
Résumé	Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.	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.