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| Półnadzorowany model mieszanin Gaussa× | Propagacja etykiet× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2000 | 2002 |
| Twórca≠ | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. | Zhu, X. & Ghahramani, Z. |
| Typ≠ | Generative semi-supervised classifier | Graph-based semi-supervised classification |
| Źródło pierwotne≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Inne nazwy | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Pokrewne | 3 | 3 |
| Podsumowanie≠ | 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. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
| ScholarGateZbiór danych ↗ |
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