Comparer des méthodes

	Apprentissage métrique d'ensemble	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	1990s–2004
Auteur d'origine	Multiple contributors (Weinberger, Saul, et al.)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble of learned distance metrics	Ensemble (combination of multiple classifiers by vote)
Source fondatrice	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
Alias	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	5	5
Résumé	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.