Comparer des méthodes

	Apprentissage semi-supervisé bayésien	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2003–2006	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic semi-supervised framework	Probabilistic non-parametric model
Source fondatrice	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	6	3
Résumé	Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.	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.