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。
| 正则化半监督学习× | 半监督学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006 | 1970s–2006 (formalized) |
| 提出者≠ | Belkin, M.; Niyogi, P.; Sindhwani, V. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Regularized learning paradigm | Learning paradigm |
| 开创性文献≠ | Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关≠ | 6 | 5 |
| 摘要≠ | Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGate数据集 ↗ |
|
|