On the generalization ability of prototype-based classifiers with local relevance determination

We extend a recent variant of the prototype-based classifer learning vector quantization to a scheme which locally adapts relevance terms during learning. We derive explicit dimensionality-independent large-margin generalization bounds for this classifer and show that the method can be seen as margin maximizer.