Comparer des méthodes

	Apprentissage métrique d'ensemble	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	2001
Auteur d'origine	Multiple contributors (Weinberger, Saul, et al.)	Breiman, L.
Type	Ensemble of learned distance metrics	Ensemble (bagging of decision trees)
Source fondatrice	Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.