• DocumentCode
    2970984
  • Title

    Hybrid learning vector quantization

  • Author

    Lai, Yuan-Cheng ; Yu, Shiaw-Shian ; Chou, Sheng-Lin

  • Author_Institution
    Comput. & Commun. Res. Labs., Ind. Technol. Res. Inst., Hsinchu, Taiwan
  • Volume
    3
  • fYear
    1993
  • fDate
    25-29 Oct. 1993
  • Firstpage
    2587
  • Abstract
    In this paper, a hybrid learning vector quantization algorithm is proposed. It modifies both the position of representative points and normalization parameters. Some of the experiments are operated on the synthetic and real data. The results show that the proposed hybrid learning vector quantization algorithm is applicable.
  • Keywords
    learning (artificial intelligence); neural nets; pattern classification; vector quantisation; hybrid learning vector quantization; normalization parameters; representative points; Clustering algorithms; Computer networks; Decision theory; Nearest neighbor searches; Neural networks; Neurons; Pattern classification; Unsupervised learning; Vector quantization; Zinc;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
  • Print_ISBN
    0-7803-1421-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.1993.714253
  • Filename
    714253
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2970984