Comparer des méthodes

	SVM univarié bayésien	Processus Gaussien Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001–2010	1978–2006
Auteur d'origine	Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic anomaly detection	Probabilistic kernel model
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.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Bayesian OCSVM, Bayesian one-class classifier, probabilistic one-class SVM, Bayes-OCSVM	GP regression, GPR, Gaussian process model, GP classifier
Apparentées	6	3
Résumé	Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decision, making the approach more suitable when downstream actions depend on how confident the model is that a new observation is anomalous.	A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.