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	Aktywny Uczący się Model Mieszanin Gaussowskich	Uczenie ze wsparciem częściowym
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2000s (combination)	1970s–2006 (formalized)
Twórca	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Active learning for probabilistic clustering / density estimation	Learning paradigm
Źródło pierwotne	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
Inne nazwy	AL-GMM, active GMM, query-by-committee GMM, active density estimation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Pokrewne	4	5
Podsumowanie	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.