So sánh phương pháp

	Tập hợp xếp chồng Bayes	Voting Ensemble
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2018	1990s–2004
Người khởi xướng	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Loại	Bayesian ensemble combination	Ensemble (combination of multiple classifiers by vote)
Công trình gốc	Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Tên gọi khác	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Liên quan	6	5
Tóm tắt	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.