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| Aktiivse õppe Gaussi segumudel× | Pooljuhendatud Gaussi mudel× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2000s (combination) | 2000 |
| Looja≠ | Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977) | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| Tüüp≠ | Active learning for probabilistic clustering / density estimation | Generative semi-supervised classifier |
| Algallikas≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Rööpnimetused | AL-GMM, active GMM, query-by-committee GMM, active density estimation | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier |
| Seotud≠ | 4 | 3 |
| Kokkuvõte≠ | 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 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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