%0 Journal Article
%T ECOC-DRF: Discriminative random fields based on error correcting output codes
%A Francesco Ciompi
%A Oriol Pujol
%A Petia Radeva
%J Pattern Recognition
%D 2014
%V 47
%N 6
%F Francesco Ciompi2014
%O LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2470), last updated on Wed, 04 Feb 2015 16:48:11 +0100
%X 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.
%K Discriminative random fields
%K Error-correcting output codes
%K Multi-class classification
%K Graphical models
%U http://dx.doi.org/10.1016/j.patcog.2013.12.007
%P 2193-2204