方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 半监督逻辑回归× | 标签传播× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1995–2000 | 2002 |
| 提出者≠ | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) | Zhu, X. & Ghahramani, Z. |
| 类型≠ | Semi-supervised classifier | Graph-based semi-supervised classification |
| 开创性文献≠ | 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 ↗ |
| 别名 | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|