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Method for Improving Gradient Boosting Learning Efficiency Based on Modified Loss Functions

Method for Improving Gradient Boosting Learning Efficiency Based on Modified Loss Functions We consider a new method to improve the quality of training in gradient boosting as wellas to increase its generalization performance based on the use of modified loss functions. Incomputational experiments, the possible applicability of this method to improve the quality ofgradient boosting when solving various classification and regression problems on real data isshown. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automation and Remote Control Springer Journals

Method for Improving Gradient Boosting Learning Efficiency Based on Modified Loss Functions

Automation and Remote Control , Volume 83 (12) – Dec 1, 2022

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References (14)

Publisher
Springer Journals
Copyright
Copyright © Pleiades Publishing, Ltd. 2022
ISSN
0005-1179
eISSN
1608-3032
DOI
10.1134/s00051179220120074
Publisher site
See Article on Publisher Site

Abstract

We consider a new method to improve the quality of training in gradient boosting as wellas to increase its generalization performance based on the use of modified loss functions. Incomputational experiments, the possible applicability of this method to improve the quality ofgradient boosting when solving various classification and regression problems on real data isshown.

Journal

Automation and Remote ControlSpringer Journals

Published: Dec 1, 2022

Keywords: gradient boosting; decision tree; loss function; machine learning; data analysis

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