Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beiziešu Gausa maisījuma modelis× | Pusuzraudzības Gausa maisījuma modelis× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1999–2006 | 2000 |
| Autors≠ | Attias, H.; Bishop, C. M. | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| Tips≠ | Probabilistic clustering / density estimation | Generative semi-supervised classifier |
| Pirmavots≠ | Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2 | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | 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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