方法证据记录
Bayesian Bagging
Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy.
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 Bagging (Bootstrap Aggregation with Bayesian Bootstrap)
分类方法记录 · ml-model / machine-learning
- Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001). · URL
- Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9(1), 130–134. · DOI 10.1214/aos/1176345338
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