Online Gaussian Mixture Model
Online Gaussian Mixture Model tilpasser den klassiske GMM til strømmende eller store datamængder ved at erstatte fuld-batch EM med inkrementelle opdateringer — behandler én observation eller mini-batch ad gangen og kontinuerligt forfiner komponentmidler, kovarianser og blandingsvægte uden at genbesøge hele datasættet.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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. DOI: 10.1111/j.1467-9868.2009.00698.x ↗
- Sato, M. & Ishii, S. (2000). On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2), 407–432. DOI: 10.1162/089976600300015853 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Online Gaussian Mixture Model (Incremental / Streaming GMM). ScholarGate. https://scholargate.app/da/machine-learning/online-gaussian-mixture-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bayesiansk Gaussisk Blanding (Bayesian Gaussian Mixture Model)Maskinlæring↔ compare
- K-means ClusteringMaskinlæring↔ compare
- Online K-meansMaskinlæring↔ compare
- Online læringMaskinlæring↔ compare
- Semi-supervised Gaussian Mixture ModelMaskinlæring↔ compare
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