Comparer des méthodes

	Détection d'anomalies par autoencodeur bayésien	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2014–2015	1999–2001
Auteur d'origine	Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Probabilistic generative model for unsupervised anomaly detection	Anomaly / novelty detection (unsupervised)
Source fondatrice	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	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	Bayesian VAE anomaly detection, probabilistic autoencoder anomaly detection, variational autoencoder anomaly detection, VAE-based outlier detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	5	3
Résumé	Bayesian Autoencoder Anomaly Detection uses a Variational Autoencoder — a probabilistic generative model trained on normal data — to flag anomalies by their high reconstruction error or low likelihood under the learned distribution. By treating the latent space as a probability distribution rather than a fixed point, it delivers principled uncertainty estimates alongside each anomaly score, making it especially valuable in high-stakes detection tasks.	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.