• DocumentCode
    2167960
  • Title

    A self-deleting neural network for vector quantization

  • Author

    Maeda, Michiharu ; Miyajima, Hiromi ; Murashima, Sadayuki

  • Author_Institution
    Fac. of Eng., Kagoshima Univ., Japan
  • fYear
    1996
  • fDate
    18-21 Nov 1996
  • Firstpage
    18
  • Lastpage
    21
  • Abstract
    Vector quantization is required the algorithm that minimizes the distortion error, and used for both storage and transmission of speech and image data. For a neural vector quantization, the self-creating neural network and self-deleting neural network and known for showing fine characters. In this paper, we improve the self-deleting neural network, and propose a generalization algorithm combining the creating and deleting neural networks. We discuss algorithms with neighborhood relations compared with the proposed one. Experimental results show the effectiveness of the proposed algorithm
  • Keywords
    generalisation (artificial intelligence); learning (artificial intelligence); self-organising feature maps; vector quantisation; distortion error; generalization algorithm; image data; neighborhood relations; self-deleting neural network; speech data; vector quantization; Data engineering; Euclidean distance; Image storage; Neural networks; Probability density function; Speech; Tellurium; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1996., IEEE Asia Pacific Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    0-7803-3702-6
  • Type

    conf

  • DOI
    10.1109/APCAS.1996.569208
  • Filename
    569208