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Adaptive Higher Order Neural networks for Effective data Mining
Citation
Xu, S and Chen, L, Adaptive Higher Order Neural networks for Effective data Mining, Sixth International Symposium on Neural Networks (ISNN 2009), 26-29 May 2009, Wuhan, China, pp. 165-173. ISBN 978-3-642-01216-7 (2009) [Refereed Conference Paper]
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Copyright Statement
The original publication is available at www.springerlink.com
DOI: doi:10.1007/978-3-642-01216-7_18
Abstract
A new adaptive Higher Order Neural Network (HONN) is introduced and applied in data mining tasks such as determining automobile yearly losses and edible mushrooms. Experiments demonstrate that the new adaptive HONN model offers advantages over conventional Artificial Neural Network (ANN) models such as higher generalization capability and the ability in handling missing values in a dataset. A new approach for determining the best number of hidden neurons is also proposed.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | neural network, higher order neural network, adaptive activation function, data mining |
| 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: | 60450 |
| Year Published: | 2009 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2010-02-05 |
| Last Modified: | 2015-02-13 |
| Downloads: | 2 View Download Statistics |
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