Salīdzināt metodes

	Tiešsaistes Gausa maisījuma modelis	Tiešsaistes apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000–2009	1958–2000s
Autors	Cappé, O. & Moulines, E. (online EM formulation)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tips	Probabilistic clustering / density estimation (incremental)	Learning paradigm (sequential model update)
Pirmavots	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Citi nosaukumi	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	incremental learning, sequential learning, streaming learning, online machine learning
Saistītās	5	6
Kopsavilkums	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.