• DocumentCode
    395136
  • Title

    Multi-branch structure of layered neural networks

  • Author

    Yamashita, Takashi ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Jnichi

  • Author_Institution
    Graduate Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan
  • Volume
    1
  • fYear
    2002
  • fDate
    18-22 Nov. 2002
  • Firstpage
    243
  • Abstract
    In this paper, a multi-branch structure of neural networks is studied to make their size compact. The multi-branch structure has shown improved performance against conventional neural networks. As a result, it has been proved that the number of nodes of networks and the computational cost for training networks can be reduced. In addition, it could be said that proposed multi-branch networks are special cases of higher order neural networks, however, they obtain higher order effect easier without suffering the parameter explosion problem.
  • Keywords
    neural nets; computational cost; higher order neural networks; layered neural networks; multibranch structure; network training; Backpropagation algorithms; Computational efficiency; Computer networks; Costs; Explosions; Gradient methods; Information science; Neural networks; Production systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
  • Print_ISBN
    981-04-7524-1
  • Type

    conf

  • DOI
    10.1109/ICONIP.2002.1202170
  • Filename
    1202170