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Online Gaussian Mixture Model/Evidence
Method evidence record

Online Gaussian Mixture Model

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.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Online Gaussian Mixture Model (Incremental / Streaming GMM)
Taxonomic method record · ml-model / machine-learning
  • 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
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

No curated claims yet

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketBayesian Gaussian Mixture Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketK-meansmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOnline K-meansmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOnline Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised Gaussian Mixture Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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