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Abstract: We extend a recent variant of the prototype-based classifier learning vector quantization to a scheme which locally adapts relevance terms during learning. We derive explicit dimensionality-independent large-margin generalization bounds for this classifier and show that the method can be seen as margin maximizer. (Update)
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BibTeX entry: (Update)
@misc{ hammer-generalization,
author = "Barbara Hammer and et al.",
title = "On the generalization ability of prototype-based classifiers with local
relevance determination",
url = "citeseer.ist.psu.edu/article/hammer05generalization.html" }
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1 Prototype recognition of splice sites (context) - Hammer, Strickert et al. - 2005
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