Comparer des méthodes

	Apprentissage semi-supervisé bayésien	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2003–2006	1970s–2006 (formalized)
Auteur d'origine	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Probabilistic semi-supervised framework	Learning paradigm
Source fondatrice	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	6	5
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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.