How large should ensembles of classifiers be?

DOI10.1016/j.patcog.2012.10.021zbMath1264.68128OpenAlexW2011876338MaRDI QIDQ1951252

Alberto Suárez, Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz

Publication date: 4 June 2013

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10486/664125

zbMATH Keywords

baggingrandom forestensemble learningasymptotic ensemble predictionensemble size

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (8)

Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting ⋮ Weighted classifier ensemble based on quadratic form ⋮ Double random forest ⋮ Comparison of machine learning methods for copper ore grade estimation ⋮ Estimating the algorithmic variance of randomized ensembles via the bootstrap ⋮ Unnamed Item ⋮ How to adjust an ensemble size in stream data mining? ⋮ Estimating a sharp convergence bound for randomized ensembles

This page was built for publication: How large should ensembles of classifiers be?