DocumentCode :
2463978
Title :
A Constructive Algorithm for Training Neural Network Ensemble
Author :
Dong, Jianming ; Yang, Qifan ; Hu, Jueliang ; Jiang, Yiwei ; Li, Wang
Author_Institution :
Dept. of Math., Zhejiang Univ., Hangzhou, China
Volume :
3
fYear :
2010
fDate :
16-17 Dec. 2010
Firstpage :
129
Lastpage :
132
Abstract :
Neural network ensemble is a learning paradigm where many neural networks are used together to solve a particular problem. This paper presents a new method to construct a neural network ensemble (NNE) based on Correlation, Interaction Validation and Entropy (CIENNE). The method consists of two parts: a sub-algorithm to construct best component neural networks with Correlation and Interaction Validation, and a sub-algorithm to combine the component neural networks with Entropy. Experimental results demonstrate that the proposed approach is effective.
Keywords :
entropy; learning (artificial intelligence); neural nets; constructive algorithm; correlation; entropy; interaction validation; learning; problem solving; training neural network ensemble; Artificial neural networks; Bagging; Boosting; Correlation; Entropy; Neurons; Training; diversity; entropy; interaction validation; neural network ensemble;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
Type :
conf
DOI :
10.1109/GCIS.2010.25
Filename :
5709339
Link To Document :
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