方法证据记录
Bayesian Boosting
Bayesian boosting integrates probabilistic Bayesian inference with boosting ensemble techniques, combining multiple weak learners while maintaining full uncertainty quantification over predictions. Unlike standard gradient boosting that produces a single point estimate, Bayesian boosting yields a posterior distribution over the ensemble output, enabling calibrated confidence intervals alongside predictions.
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 Boosting (Probabilistic Ensemble Learning)
分类方法记录 · ml-model / machine-learning
- Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181. · URL
- Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. Annals of Applied Statistics, 4(1), 266–298. · DOI 10.1214/09-AOAS285
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