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A novel higher order artificial neural networks
Citation
Xu, S, A novel higher order artificial neural networks, Proceedings of the Second International Symposium on Computational Mechanics and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, 30 November - 3 December 2009, Hong Kong, Macau, pp. 1507-1511. ISBN 978-0-7354-0778-7 (2010) [Refereed Conference Paper]
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Copyright Statement
Copyright © 2010 American Institute of Physics
Abstract
In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | artificial neural network, data mining, neuron activation function, higher order neural network |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Information and Communication Services |
Objective Group: | Information systems, technologies and services |
Objective Field: | Information systems, technologies and services not elsewhere classified |
UTAS Author: | Xu, S (Dr Shuxiang Xu) |
ID Code: | 64941 |
Year Published: | 2010 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2010-09-16 |
Last Modified: | 2014-12-22 |
Downloads: | 0 |
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