• DocumentCode
    3038860
  • Title

    Bill money classification by competitive learning

  • Author

    Kosaka, Toshihisa ; Omatu, Sigeru

  • Author_Institution
    Glory TD Himeji, Hyougo, Japan
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    5
  • Lastpage
    9
  • Abstract
    The progress of computer science enables us to process complex and large scale computations and advanced pattern recognition methods can be adopted for pattern classification problems. Among them neuro-pattern recognition, which means pattern recognition based on neural networks, has been given attention since it has classified various patterns like human beings. We adopt the learning vector quantization (LVQ) method to classify money. The reasons for using the LVQ are that it can process unsupervised classification data and treat a large amount of input data with a small computational burden. We construct the LVQ network to classify Italian Lira. Compared with a conventional pattern matching technique, which has been adopted as a classification method, the proposed method has shown excellent classification results
  • Keywords
    financial data processing; neural nets; pattern classification; pattern matching; unsupervised learning; vector quantisation; Italian Lira classification; LVQ; competitive learning; learning vector quantization; money classification; neural networks; pattern classification; pattern matching; pattern recognition; unsupervised classification data; Biological neural networks; Computer science; Focusing; Humans; Large-scale systems; Pattern classification; Pattern matching; Pattern recognition; Pixel; Size measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing Methods in Industrial Applications, 1999. SMCia/99. Proceedings of the 1999 IEEE Midnight-Sun Workshop on
  • Conference_Location
    Kuusamo
  • Print_ISBN
    0-7803-5280-7
  • Type

    conf

  • DOI
    10.1109/SMCIA.1999.782699
  • Filename
    782699