Bandingkan metode

	Model Campuran Gaussian Daring	Model Gaussian Campuran Semi-Terawasi
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2000–2009	2000
Pencetus	Cappé, O. & Moulines, E. (online EM formulation)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Tipe	Probabilistic clustering / density estimation (incremental)	Generative semi-supervised classifier
Sumber perintis	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
Alias	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier
Terkait	5	3
Ringkasan	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.