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。
| 在线半监督学习× | 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≠ | 2000s–2010s | 1970s–2006 (formalized) |
| 提出者≠ | Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Incremental / stream-based semi-supervised learning framework | Learning paradigm |
| 开创性文献≠ | Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关≠ | 6 | 5 |
| 摘要≠ | Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts. | 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 ↗ |
|
|