Title :
A self-deleting neural network for vector quantization
Author :
Maeda, Michiharu ; Miyajima, Hiromi ; Murashima, Sadayuki
Author_Institution :
Fac. of Eng., Kagoshima Univ., Japan
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;
Conference_Titel :
Circuits and Systems, 1996., IEEE Asia Pacific Conference on
Conference_Location :
Seoul
Print_ISBN :
0-7803-3702-6
DOI :
10.1109/APCAS.1996.569208