Ensemble learning by negative correlation learning

Huanhuan CHEN; Anthony G. COHN; Xin YAO

doi:10.1007/9781441993267_6

Ensemble learning by negative correlation learning

Huanhuan CHEN^*, Anthony G. COHN, Xin YAO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

6 Citations (Scopus)

Abstract

This chapter investigates a specific ensemble learning approach by negative correlation learning (NCL) [21–23]. NCL is an ensemble learning algorithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.

Original language	English
Title of host publication	Ensemble Machine Learning: Methods and Applications
Editors	Cha ZHANG, Yunqian MA
Publisher	Springer New York
Pages	177-201
Number of pages	25
Edition	1
ISBN (Electronic)	9781441993267
ISBN (Print)	9781441993250
DOIs	https://doi.org/10.1007/9781441993267_6
Publication status	Published - 2012
Externally published	Yes

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© Springer Science+Business Media, LLC 2012. All rights reserved.

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10.1007/9781441993267_6

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AB - This chapter investigates a specific ensemble learning approach by negative correlation learning (NCL) [21–23]. NCL is an ensemble learning algorithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.

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Ensemble learning by negative correlation learning

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