Quantizing for minimum average misclassification risk.
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.
Diamantini
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Istituto di Informatica, Dipartimento di Elettronica, Universitá di Ancona, I-60131 Ancona, Italy.
Spalvieri
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