Comparer des méthodes

	Ensemble One-Class SVM	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1999–2001
Auteur d'origine	Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base)	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Ensemble anomaly detector	Anomaly / novelty detection (unsupervised)
Source fondatrice	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.	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	Ensemble OC-SVM, multiple one-class SVM, OC-SVM ensemble, one-class SVM committee	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	4	3
Résumé	Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts a single one-class SVM, producing a more stable and accurate novelty or outlier detector.	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.