On a method for constructing ensembles of regression models
type: Item
instance of: scholarly article
title: On a method for constructing ensembles of regression models
zbMATH Open document ID: 1303.62030
DOI: 10.1134/S0005117913100044
author: E. V. Burnaev
author: P. V. Prikhod'ko
published in: Automation and Remote Control
publication date: 2014-10-15T00:00:00Z
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62J99
zbMATH DE Number: 6356114
describes a project that uses: AdaBoost.MH
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
cites work: Bagging predictors
cites work: Boosting methods for regression
cites work: Greedy function approximation: A gradient boosting machine.
cites work: Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
cites work: Ordered risk minimization. I
cites work: Neural Network Learning
cites work: Q4938227
cites work: Ensembling neural networks: Many could be better than all
cites work: An Algorithm for Least-Squares Estimation of Nonlinear Parameters
cites work: Benchmarking optimization software with performance profiles.
cites work: Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
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description: scientific article; zbMATH DE number 6356114