Margin Analysis of the LVQ Algorithm
Abstract
Prototypes based algorithms are commonly used to reduce the computa- tional 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 sug- gest 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 the 20 years old Learning Vector Quantization (LVQ) algorithm emerges natu- rally from our framework.
Cite
Text
Crammer et al. "Margin Analysis of the LVQ Algorithm." Neural Information Processing Systems, 2002.Markdown
[Crammer et al. "Margin Analysis of the LVQ Algorithm." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/crammer2002neurips-margin/)BibTeX
@inproceedings{crammer2002neurips-margin,
title = {{Margin Analysis of the LVQ Algorithm}},
author = {Crammer, Koby and Gilad-bachrach, Ran and Navot, Amir and Tishby, Naftali},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {479-486},
url = {https://mlanthology.org/neurips/2002/crammer2002neurips-margin/}
}