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A novel approach for determining the optimal number of hidden layer neurons for FNN's and its application in data mining
Optimizing the number of hidden layer neurons for an FNN (feedforward neural network) to solve a practical problem remains one of the unsolved tasks in this research area. In this paper we review several mechanisms in the neural networks literature which have been used for determining an optimal number of hidden layer neuron (given an application), propose our new approach based on some mathematical evidence, and apply it in financial data mining. Compared with the existing methods, our new approach is proven (with mathematical justification), and can be easily handled by users from all application fields.
History
Publication title
Proceedings The 5th International Conference on Information Technology and ApplicationsEditors
Liang, YCPagination
683-686ISBN
978-0-9803267-2-7Department/School
School of Information and Communication TechnologyPublisher
iCITAPlace of publication
Carins, QldEvent title
International Conference on Information Technology and Applications: iCITAEvent Venue
Carins, QldDate of Event (Start Date)
2008-06-23Date of Event (End Date)
2008-06-26Rights statement
Copyright 2008 ICITARepository Status
- Restricted