Journal
NEUROCOMPUTING
Volume 72, Issue 10-12, Pages 2227-2234Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2008.12.028
Keywords
Activation function; Classification; Complex-valued neural network; Ensemble
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Funding
- Japanese Society for Promotion of Science (JSPS)
- Yazaki Memorial Foundation
- University of Fukui
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This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algorithm was used to train the component CVNNs. We applied two ensemble methods, negative correlation learning and bagging, to create the ensembles. Experimental results on a number of real-world benchmark problems showed a substantial performance improvement of the ensembles over the individual single-layered CVNN classifiers. Furthermore, the generalization performances were nearly equivalent to those obtained by the ensembles of real-valued multilayer neural networks. (C) 2009 Elsevier B.V. All rights reserved.
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