विधियों की तुलना करें

	सक्रिय शिक्षण गॉसियन मिश्रण मॉडल	अर्ध-पर्यवेक्षित गॉसियन मिश्रण मॉडल
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2000s (combination)	2000
प्रवर्तक	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
प्रकार	Active learning for probabilistic clustering / density estimation	Generative semi-supervised classifier
मौलिक स्रोत	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
उपनाम	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
संबंधित	4	3
सारांश	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.