Comparar métodos

	Modelo de Mezcla Gaussiana con Aprendizaje Activo	Modelo de Mezcla Gaussiana Semi-supervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2000s (combination)	2000
Autor original	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Tipo	Active learning for probabilistic clustering / density estimation	Generative semi-supervised classifier
Fuente 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
Alias	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
Relacionados	4	3
Resumen	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.