Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model Gaussian de Amestec Online× | Modelul Gaussian Mixt Semi-Supervizat× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2000–2009 | 2000 |
| Autorul original≠ | Cappé, O. & Moulines, E. (online EM formulation) | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| Tip≠ | Probabilistic clustering / density estimation (incremental) | Generative semi-supervised classifier |
| Sursa seminală≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Denumiri alternative | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. |
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