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Adaptive Higher Order Neural Networks for Effective Data Mining

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The Sixth International Symposium on Neural Networks (ISNN 2009)

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 56))

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.

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Author information

Authors and Affiliations

  1. School of Computing and IS, University of Tasmania, Launceston, Tasmania, 7250, Australia

    Shuxiang Xu

  2. Department of Health and Human Services, Hobart, Information Services, Tasmania, 7000, Australia

    Ling Chen

Authors
  1. Shuxiang Xu
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  2. Ling Chen
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Editor information

Editors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, No. 1037, Luoyu Road, 430074, Wuhan, Hubei, China

    Hongwei Wang , Yi Shen  & Zhigang Zeng ,  & 

  2. Texas A&M University at Qatar, PO Box 23874, Doha, Qatar,  

    Tingwen Huang

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Xu, S., Chen, L. (2009). Adaptive Higher Order Neural Networks for Effective Data Mining. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-01216-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01215-0

  • Online ISBN: 978-3-642-01216-7

  • eBook Packages: EngineeringEngineering (R0)

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