Compara mètodes

	Model Mixture Gaussiana amb Aprenentatge Actiu	Aprenentatge semi-supervisat
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2000s (combination)	1970s–2006 (formalized)
Autor original	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tipus	Active learning for probabilistic clustering / density estimation	Learning paradigm
Font seminal	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Àlies	AL-GMM, active GMM, query-by-committee GMM, active density estimation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Relacionats	4	5
Resum	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.	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.