Machine learningMachine learning
贝叶斯堆叠集成
贝叶斯堆叠通过寻找非负权重来组合多个基础模型的预测分布，这些权重最大化了混合模型的留一法（leave-one-out）对数预测得分。该方法由 Yao, Vehtari, Simpson, 和 Gelman (2018) 正式化，它产生一个单一的、校准良好的预测分布，在交叉验证下，其性能被证明至少与任何单一组成模型一样好。
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来源
- Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007. DOI: 10.1214/17-BA1091 ↗
- Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. DOI: 10.1016/S0893-6080(05)80023-1 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Stacking Ensemble (Bayesian Stacking of Predictive Distributions). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-stacking-ensemble
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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