On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines
Abstract
A common way of constructing a multiclass classifier is by combining the outputs of several binary ones, according to an error-correcting output code (ECOC) scheme. The combination is typically done via a simple nearest-neighbor rule that finds the class that is closest in some sense to the outputs of the binary classifiers. For these nearest-neighbor ECOCs, we improve existing bounds on the error rate of the multiclass classifier given the average binary distance. The new bounds provide insight into the one-versus-rest and all-pairs matrices, which are compared through experiments with standard datasets. The results also show why elimination (also known as DAGSVM) and Hamming decoding often achieve the same accuracy.
Cite
Text
Klautau et al. "On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines." Journal of Machine Learning Research, 2003.Markdown
[Klautau et al. "On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines." Journal of Machine Learning Research, 2003.](https://mlanthology.org/jmlr/2003/klautau2003jmlr-nearestneighbor/)BibTeX
@article{klautau2003jmlr-nearestneighbor,
title = {{On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines}},
author = {Klautau, Aldebaro and Jevtić, Nikola and Orlitsky, Alon},
journal = {Journal of Machine Learning Research},
year = {2003},
pages = {1-15},
volume = {4},
url = {https://mlanthology.org/jmlr/2003/klautau2003jmlr-nearestneighbor/}
}