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Abstract: Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that... (Update)
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BibTeX entry: (Update)
K.Crammer, R.Gilad-Bachrach, A.Navot, and N.Tishby. Margin analysis of the LVQ algorithm, NIPS'2002. http://citeseer.ist.psu.edu/crammer02margin.html More
@misc{ crammer02margin,
author = "K. Crammer and R. Gilad-Bachrach and A. Navot and N. Tishby",
title = "Margin analysis of the LVQ algorithm",
text = "K.Crammer, R.Gilad-Bachrach, A.Navot, and N.Tishby. Margin analysis of
the LVQ algorithm, NIPS'2002.",
year = "2002",
url = "citeseer.ist.psu.edu/crammer02margin.html" }
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