Machine learningMachine learning
集成高斯过程
集成高斯过程（Ensemble Gaussian Process）在数据子集或重叠区域上训练多个独立的高斯过程（GP）专家模型，然后将它们的后验预测——均值和方差——组合成一个单一的概率预测。这种方法保留了标准GP的校准不确定性估计，同时克服了其O(n³)的立方计算成本瓶颈，使得在拥有数千到数百万观测值的数据集上进行概率回归变得可行。
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来源
- Tresp, V. (2000). A Bayesian Committee Machine. Neural Computation, 12(11), 2719–2741. DOI: 10.1162/089976600300014908 ↗
- Deisenroth, M. P., & Ng, J. W. (2015). Distributed Gaussian Processes. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 1481–1490. link ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble of Gaussian Processes (Committee / Distributed GP). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-gaussian-process
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