Machine learningMachine learning
投票集成 (Voting Ensemble)
投票集成通过独立训练多个不同的分类器，并以投票方式组合它们的预测来工作：硬投票选择多数模型选择的类别，而软投票则平均它们的类别概率估计，可以选择性地加入模型权重。组合后的预测通常优于任何单个成员，并且在基础模型拟合后无需额外的训练。
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Method map
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来源
- Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
- Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol 1857, pp. 1–15. Springer. DOI: 10.1007/3-540-45014-9_1 ↗
如何引用本页
ScholarGate. (2026, June 3). Voting Ensemble (Majority and Weighted Voting of Multiple Classifiers). ScholarGate. https://scholargate.app/zh/machine-learning/voting-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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