Vector quantization with optimized grouping and parallel distributed processing

Y. Matsuyama

研究成果: Article査読

5 被引用数 (Scopus)

抄録

Vector quantization with optimized grouping of elements is studied. The presented vector quantization allows optimal or suboptimal grouping of source data. Thus, the algorithms herein are called variable region vector quantization. The optimization yielding the data subgroups can also be interpreted as the connection weight adjustmen. The presented methods are still executable on conventional SISD computers. However, the adaptation of the variable region vector quantization to SIMD and MIMD computation via PDP (Parallel Distributed Processing) approach motivates new computational concepts and tools. Here, a fine-grain MIMD computer is emulated and used for the variable region vector quantizer design. Experimental results on digital speech and images are given.

本文言語English
ページ(範囲)36
ページ数1
ジャーナルNeural Networks
1
1 SUPPL
DOI
出版ステータスPublished - 1988
外部発表はい

ASJC Scopus subject areas

  • 人工知能
  • 神経科学(全般)

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