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Ensemble Learning via Negative Correlation by Y. Liu; X. Yao is a Computer Science article available to read on EtoBox.

What is Ensemble Learning via Negative Correlation about?

This paper presents a learning approach, i.e. negative correlation learning, for neural network ensembles. Unlike previous learning approaches for neural network ensembles, negative correlation learning attempts to train individual networks in an ensemble and combines them in the same learning process. In negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms in their error functions. Rather than producing unbiased individual networks whose errors are uncorrelated, negative correlation learning can create negatively correlated networks to encourage specialisation and cooperation among the individual networks. Empirical studies have been carried out to show why and how negative correlation learning works. The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.

Who reads Ensemble Learning via Negative Correlation?

Author

Y. Liu; X. Yao

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)

Published

1999

Language

EN

Field

Computer Science (Physical Sciences)