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

Hammer, Barbara GND; Schleif, Frank-Michael GND; Villmann, Thomas

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.

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Hammer, Barbara / Schleif, Frank-Michael / Villmann, Thomas: On the generalization ability of prototype-based classifiers with local relevance determination. Clausthal-Zellerfeld 2005. Institut für Informatik.

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