Usporedite metode

	Učenje ansambla metrike	Slučajna šuma
Područje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning
Godina nastanka	2000s–2010s	2001
Tvorac	Multiple contributors (Weinberger, Saul, et al.)	Breiman, L.
Vrsta	Ensemble of learned distance metrics	Ensemble (bagging of decision trees)
Temeljni izvor	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.
Drugi nazivi	EML, ensemble distance metric learning, multiple metric fusion, combined metric learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Srodne	5	4
Sažetak	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.