Σύγκριση μεθόδων

	Bayesian One-Class SVM	Διαδικασία Γκάους
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001–2010	2006 (book); roots in Kriging, 1951)
Δημιουργός	Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others	Rasmussen, C. E. & Williams, C. K. I.
Τύπος	Probabilistic anomaly detection	Probabilistic non-parametric model
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Bayesian OCSVM, Bayesian one-class classifier, probabilistic one-class SVM, Bayes-OCSVM	GP, Gaussian Process Regression, GPR, Kriging
Συναφείς	6	3
Σύνοψη	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 Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.