방법 비교

	앙상블 거리 학습	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000s–2010s	1990s–2004
창시자	Multiple contributors (Weinberger, Saul, et al.)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble of learned distance metrics	Ensemble (combination of multiple classifiers by vote)
원전	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
별칭	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	5	5
요약	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.