Comparer des méthodes

	Apprentissage semi-supervisé bayésien	Apprentissage Actif Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2003–2006	1992–2011
Auteur d'origine	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	MacKay, D.J.C.; Houlsby, N. et al.
Type	Probabilistic semi-supervised framework	Active learning with Bayesian uncertainty
Source fondatrice	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.
Alias	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning
Apparentées	6	6
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.	Bayesian Active Learning (BAL) combines a probabilistic model with an active query strategy to identify the unlabeled examples that, once labeled, would most reduce model uncertainty. Instead of labeling data at random, BAL guides an oracle — typically a human annotator — toward the points where labeling will provide the greatest information gain, making it highly label-efficient.