Machine learningMachine learning
正则化高斯过程
正则化高斯过程（GP）是一种基于核函数的概率模型，它在函数上放置先验，并通过噪声正则化参数——观测噪声方差——来显式控制过拟合，该参数可防止模型记忆训练标签。它在预测的同时产生校准的不确定性估计，使其特别适用于小型或昂贵的数据集，在这些数据集中，了解模型有多自信与预测本身同等重要。
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来源
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
- Scholkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press. ISBN: 978-0-262-19475-4
如何引用本页
ScholarGate. (2026, June 3). Regularized Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/regularized-gaussian-process
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