方法证据记录
Bayesian Stacking Ensemble
Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Stacking Ensemble (Bayesian Stacking of Predictive Distributions)
分类方法记录 · ml-model / machine-learning
- 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
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