Linganisha mbinu

	Active Learning Gaussian Mixture Model	Kielelezo cha Mchanganyiko wa Gaussian chenye Usimamizi Kidogo
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s (combination)	2000
Mwanzilishi	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Aina	Active learning for probabilistic clustering / density estimation	Generative semi-supervised classifier
Chanzo asilia	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
Majina mbadala	AL-GMM, active GMM, query-by-committee GMM, active density estimation	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier
Zinazohusiana	4	3
Muhtasari	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 Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce.