方法对比

	集成度量学习	投票集成 (Voting Ensemble)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000s–2010s	1990s–2004
提出者	Multiple contributors (Weinberger, Saul, et al.)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
类型	Ensemble of learned distance metrics	Ensemble (combination of multiple classifiers by vote)
开创性文献	Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
别名	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
相关	5	5
摘要	Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning.	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.