Comparer des méthodes

	Processus Gaussien Régularisé	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (canonical formulation); kernel regularization roots 1990s	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I.	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic kernel model with regularization	Probabilistic non-parametric model
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	4	3
Résumé	A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.	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.