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Quadratic programming algorithms for ensemble models

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Abstract Ensemble models, such as bagging, random forests, and boosting, have better predictive accuracy than single classifiers. These ensembles typically consist of hundreds of single classifiers, which make future predictions and model interpretation much more difficult than for single classifiers. Recently, research efforts have been directed toward improving ensembles by reducing their size while increasing or maintaining their predictive accuracy. In this article, we review recently proposed methods based on quadratic programming techniques for accomplishing these goals. WIREs Comput Stat 2013, 5:41–47. doi: 10.1002/wics.1237 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Algorithms and Computational Methods > Quadratic and Nonlinear Programming

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Algorithms and Computational Methods > Quadratic and Nonlinear Programming
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

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