পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং গাউসিয়ান মিক্সচার মডেল	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s (combination)	1970s–2006 (formalized)
প্রবর্তক	Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Active learning for probabilistic clustering / density estimation	Learning paradigm
মৌলিক উৎস	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
অপর নাম	AL-GMM, active GMM, query-by-committee GMM, active density estimation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	4	5
সারসংক্ষেপ	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.