• DocumentCode
    1749088
  • Title

    General properties of the generalised Lotto-type competitive learning

  • Author

    Luk, Andrew ; Lien, Sandra

  • Author_Institution
    St B&P Neural investments Pty Ltd., Australia
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    418
  • Abstract
    We highlight the difficulty of detecting small and rare clusters. Although in theory it is possible for the algorithms to follow the source density function, we note that in experiment it is very difficult to locate these smaller clusters, especially if the prototype set is limited. We propose to train the network iteratively with the same prototype set. This simple method enable one to locate these smaller clusters, albeit with the possibility of over-training
  • Keywords
    iterative methods; neural nets; unsupervised learning; Lotto-type learning; clusters; competitive learning; iterative method; source density function; Australia; Clustering algorithms; Convergence; Density functional theory; Frequency; Investments; Iterative algorithms; Neural networks; Neurons; Prototypes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939056
  • Filename
    939056