On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination
Barbara Hammer, Frank-Michael Schleif, T. Villmann · 2005
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.