Second International Conference on Semantics, Knowledge, and Grid (SKG'06) Knowledge Discovery and Integration Based on A Novel Neural Network Ensemble Model Guilin, Guangxi, China November 01-November 03 ISBN: 0-7695-2673-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SKG.2006.59
This article explores the utility of neural network ensembles in knowledge discovery and integration. A novel neural network ensemble model KBNNE (Knowledge-Based Neural Network Ensembles) integrating KDD (Knowledge Discovery in Database) techniques and neural network modeling algorithms by "parallel operations" is proposed. Through balancing the relative importance of knowledge learned by induction and deduction, KBNNE can avoid the knowledge loss and enhance the "transparency " of neural network models. The effectiveness of the proposed model is demonstrated through computer simulations on simple artificial problems and an actual modeling problem.
Citation:
Yong Wang, Hong-Jie Xing, "Knowledge Discovery and Integration Based on A Novel Neural Network Ensemble Model," skg, pp.9, Second International Conference on Semantics, Knowledge, and Grid (SKG'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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