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| Semi-supervised logistisk regression× | Label Propagation× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1995–2000 | 2002 |
| Ophavsperson≠ | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) | Zhu, X. & Ghahramani, Z. |
| Type≠ | Semi-supervised classifier | Graph-based semi-supervised classification |
| Oprindelig kilde≠ | Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Aliasser | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset. | 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. |
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