• DocumentCode
    1945579
  • Title

    Application of New Adaptive Higher Order Neural Networks in Data Mining

  • Author

    Xu, Shuxiang ; Chen, Ling

  • Author_Institution
    Sch. of Comput., Univ. of Tasmania, Launceston, TAS
  • Volume
    1
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    115
  • Lastpage
    118
  • Abstract
    This paper introduces an adaptive Higher Order Neural Network (HONN) model and applies it in data mining such as simulating and forecasting government taxation revenues. The proposed adaptive HONN model offers significant advantages over conventional Artificial Neural Network (ANN) models such as much reduced network size, faster training, as well as much improved simulation and forecasting errors. The generalization ability of this HONN model is explored and discussed. A new approach for determining the best number of hidden neurons is also proposed.
  • Keywords
    data mining; neural nets; adaptive higher order neural networks; data mining; hidden neurons; Artificial neural networks; Brain modeling; Computational modeling; Computer networks; Computer science; Data mining; Humans; Neural networks; Neurons; Predictive models; Data Mining; Higher Order Neural Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.897
  • Filename
    4721705