Comparer des méthodes

	Processus Gaussien semi-supervisé	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2004	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Lawrence, N. D. & Jordan, M. I.	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic model (semi-supervised)	Probabilistic non-parametric model
Source fondatrice	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	5	3
Résumé	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.	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.