On a method for constructing ensembles of regression models

DOI10.1134/S0005117913100044zbMath1303.62030MaRDI QIDQ462080

P. V. Prikhod'ko, E. V. Burnaev

Publication date: 15 October 2014

Published in: Automation and Remote Control (Search for Journal in Brave)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear inference, regression (62J99)

Related Items (1)

ON THE CONVERGENCE RATE OF THE SUBGRADIENT METHOD WITH METRIC VARIATION AND ITS APPLICATIONS IN NEURAL NETWORK APPROXIMATION SCHEMES

Uses Software

• AdaBoost.MH
• ElemStatLearn

Cites Work

• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Ordered risk minimization. I
• Ensembling neural networks: Many could be better than all
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Neural Network Learning
• Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
• Benchmarking optimization software with performance profiles.
• Boosting methods for regression

This page was built for publication: On a method for constructing ensembles of regression models