@Article{FrancescoCiompi2014,
author="Francesco Ciompi
and Oriol Pujol
and Petia Radeva",
title="ECOC-DRF: Discriminative random fields based on error correcting output codes",
journal="Pattern Recognition",
year="2014",
volume="47",
number="6",
pages="2193--2204",
optkeywords="Discriminative random fields",
optkeywords="Error-correcting output codes",
optkeywords="Multi-class classification",
optkeywords="Graphical models",
abstract="We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.",
optnote="LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2470), last updated on Wed, 04 Feb 2015 16:48:11 +0100",
doi="10.1016/j.patcog.2013.12.007"
}