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
Link To Document