@Article{SergioEscalera2010,
author="Sergio Escalera
and Oriol Pujol
and Petia Radeva",
title="Error-Correcting Output Codes Library",
journal="Journal of Machine Learning Research",
year="2010",
volume="11",
pages="661--664",
abstract="(Feb):661\&\#8722;664In 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, $\beta$-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.",
optnote="MILAB;HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1286), last updated on Thu, 18 Jan 2018 12:01:57 +0100",
issn="1532-4435",
opturl="http://dl.acm.org/citation.cfm?id=1756026"
}