Error-Correcting Output Codes Library
Abstract
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
Cite
Text
Escalera et al. "Error-Correcting Output Codes Library." Machine Learning Open Source Software, 2010.Markdown
[Escalera et al. "Error-Correcting Output Codes Library." Machine Learning Open Source Software, 2010.](https://mlanthology.org/mloss/2010/escalera2010jmlr-errorcorrecting/)BibTeX
@article{escalera2010jmlr-errorcorrecting,
title = {{Error-Correcting Output Codes Library}},
author = {Escalera, Sergio and Pujol, Oriol and Radeva, Petia},
journal = {Machine Learning Open Source Software},
year = {2010},
pages = {661-664},
volume = {11},
url = {https://mlanthology.org/mloss/2010/escalera2010jmlr-errorcorrecting/}
}