方法对比
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| 半监督K近邻× | 半监督高斯过程× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2002 (semi-supervised extension); 1967 (KNN base) | 2004 |
| 提出者≠ | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) | Lawrence, N. D. & Jordan, M. I. |
| 类型≠ | Semi-supervised classifier / label propagation | Probabilistic model (semi-supervised) |
| 开创性文献≠ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press. link ↗ |
| 别名 | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN | SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning |
| 相关≠ | 4 | 5 |
| 摘要≠ | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. | Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive. |
| ScholarGate数据集 ↗ |
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