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| Aktiivisen oppimisen Gaussin seos-malli× | Bayesiläinen Gaussinen sekoitusmalli× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2000s (combination) | 1999–2006 |
| Kehittäjä≠ | Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977) | Attias, H.; Bishop, C. M. |
| Tyyppi≠ | Active learning for probabilistic clustering / density estimation | Probabilistic clustering / density estimation |
| Alkuperäislähde≠ | Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919. link ↗ | Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2 |
| Rinnakkaisnimet | AL-GMM, active GMM, query-by-committee GMM, active density estimation | Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing labeled set. The result is a density model that matches full-data quality while requiring far fewer labeled examples. | 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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