Comparer des méthodes

	Apprentissage semi-supervisé régularisé	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Belkin, M.; Niyogi, P.; Sindhwani, V.	Rasmussen, C. E. & Williams, C. K. I.
Type	Regularized learning paradigm	Probabilistic non-parametric model
Source fondatrice	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	6	3
Résumé	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	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.