Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tiešsaistes Gausa maisījuma modelis× | Beiziešu Gausa maisījuma modelis× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2000–2009 | 1999–2006 |
| Autors≠ | Cappé, O. & Moulines, E. (online EM formulation) | Attias, H.; Bishop, C. M. |
| Tips≠ | Probabilistic clustering / density estimation (incremental) | Probabilistic clustering / density estimation |
| 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. DOI ↗ | Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2 |
| Citi nosaukumi | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture |
| Saistītās≠ | 5 | 4 |
| 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. | The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets. |
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