• DocumentCode
    323389
  • Title

    A selective attention template matching neural network

  • Author

    Ye Xiangyun ; Feihu, Qi ; Hujun, Yin

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Shanghai Jiaotong Univ., China
  • Volume
    1
  • fYear
    1997
  • fDate
    28-31 Oct 1997
  • Firstpage
    507
  • Abstract
    A kind of similarity measure with a selective attentional property is proposed. The neural network (selective attentional template matching network, SATMN) model built with the proposed measure can be applied to template matching and classification. The model is composed of matching subnets and competing subnets, in which comparison and classification are carried out respectively. The similarity measure is available to both discrete and analog patterns. The experimental results show that it takes advantage of several widely used similarity measures in describing the variance between patterns. The neural network implementation and experimental results are given
  • Keywords
    learning (artificial intelligence); neural nets; pattern classification; pattern matching; SATMN; analog patterns; discrete patterns; experimental results; learning; pattern classification; pattern variance; selective attention template matching neural network; similarity measure; template matching; Electronic learning; Humans; Neural networks; Pattern classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-4253-4
  • Type

    conf

  • DOI
    10.1109/ICIPS.1997.672834
  • Filename
    672834