পদ্ধতির তুলনা করুন

	অনলাইন গাউসিয়ান মিক্সচার মডেল	আধা-পর্যবেক্ষিত গাউসীয় মিশ্রণ মডেল (Semi-supervised Gaussian Mixture Model)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000–2009	2000
প্রবর্তক	Cappé, O. & Moulines, E. (online EM formulation)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
ধরন	Probabilistic clustering / density estimation (incremental)	Generative semi-supervised classifier
মৌলিক উৎস	Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
অপর নাম	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier
সম্পর্কিত	5	3
সারসংক্ষেপ	Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.	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.