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- W34677718 abstract "Vector quantization with an optimal codebook is attractive for lossy data compression. So far, a number of codebook design algorithms have been proposed to minimize the mean square error, MSE. However, these algorithms have a problem that MSE minimization sometimes causes an unacceptable maximum-distortion, which is very important in several applications. This paper proposes a competitive learning algorithm with controlling maximum distortion that designs a codebook giving a maximum distortion within a given error bound. The proposed algorithm assigns a code vector to an input vector with a too large distortion. Experimental results showed that the algorithm has both maximum-distortion control and MSE minimization capability." @default.
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- W34677718 date "2005-03-20" @default.
- W34677718 modified "2023-10-14" @default.
- W34677718 title "A Competitive Learning Algorithm with Controlling Maximum Distortion" @default.
- W34677718 doi "https://doi.org/10.20965/jaciii.2005.p0166" @default.
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