Comparer des méthodes

	Processus Gaussien semi-supervisé	Processus Gaussien Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2004	1978–2006
Auteur d'origine	Lawrence, N. D. & Jordan, M. I.	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic model (semi-supervised)	Probabilistic kernel 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 regression, GPR, Gaussian process model, GP classifier
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 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.