Comparer des méthodes

	Modèle de Mélange Gaussien avec Apprentissage Actif	Modèle Bayésien de Mélange Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s (combination)	1999–2006
Auteur d'origine	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Attias, H.; Bishop, C. M.
Type	Active learning for probabilistic clustering / density estimation	Probabilistic clustering / density estimation
Source fondatrice	Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919.	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
Alias	AL-GMM, active GMM, query-by-committee GMM, active density estimation	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Apparentées	4	4
Résumé	Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing labeled set. The result is a density model that matches full-data quality while requiring far fewer labeled examples.	The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.