So sánh phương pháp

	Mô hình hỗn hợp Gauss trực tuyến	Mô hình hỗn hợp Gaussian bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000–2009	2000
Người khởi xướng	Cappé, O. & Moulines, E. (online EM formulation)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Loại	Probabilistic clustering / density estimation (incremental)	Generative semi-supervised classifier
Công trình gốc	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
Tên gọi khác	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier
Liên quan	5	3
Tóm tắt	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.