Machine learningMachine learning
稳健高斯过程
Robust Gaussian Process (Robust GP) 将标准的 Gaussian Process 框架进行了扩展，用重尾分布（通常是 Student-t 分布）取代了高斯噪声似然，从而使训练数据中的异常值对学习到的函数影响减小。它保留了标准 GP 的完整概率性和不确定性量化特性，同时对损坏或异常的观测值变得不那么敏感。
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来源
- Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257. 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). Robust Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/robust-gaussian-process
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