Comparer des méthodes

	Apprentissage semi-supervisé bayésien	Apprentissage par transfert
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2003–2006	2010 (formalized); 1990s (early roots)
Auteur d'origine	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
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	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.