Machine learningMachine learning
半监督高斯过程
半监督高斯过程将概率GP框架扩展到利用无标签数据和少量有标签观测数据。通过在函数上放置GP先验并利用无标签输入揭示的几何结构，当标签稀缺时，它能学习到比纯监督GP更准确、校准更好的预测器，因此非常适合标注成本高昂的科学和医学问题。
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来源
- 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 ↗
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-gaussian-process
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