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- W2158762024 abstract "In pattern classification, a decision rule is a labeled partition of the observation space, where labels represent classes. A way to establish a decision rule is to attach a label to each code vector of a vector quantizer (VQ). When a labeled VQ is adopted as a classifier, we have to design it in such a way that high classification performance is obtained by a given number of code vectors. In this paper we propose a learning algorithm which optimizes the position of labeled code vectors in the observation space under the minimum average misclassification risk criterion." @default.
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- W2158762024 date "1998-01-01" @default.
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- W2158762024 title "Quantizing for minimum average misclassification risk" @default.
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- W2158762024 doi "https://doi.org/10.1109/72.655039" @default.
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