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。
| 半监督主动学习× | 标签传播× | 半监督学习× | |
|---|---|---|---|
| 领域 | 机器学习 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 2002 | 2002 | 1970s–2006 (formalized) |
| 提出者≠ | Muslea, I., Minton, S., & Knoblock, C. A. | Zhu, X. & Ghahramani, Z. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Hybrid learning framework | Graph-based semi-supervised classification | Learning paradigm |
| 开创性文献≠ | Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool. 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | SSAL, active semi-supervised learning, query-based semi-supervised learning, semi-supervised learning with active queries | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关≠ | 3 | 3 | 5 |
| 摘要≠ | Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy. | 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. | 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 ↗ |
|
|
|