@inbook{ef749f482a554356b263bdcc3036f048,
title = "Multi-class classification in image analysis via error-correcting output codes",
abstract = "A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.",
author = "Sergio Escalera and Tax, {David M.J.} and Oriol Pujol and Petia Radeva and Duin, {Robert P.W.}",
year = "2011",
doi = "10.1007/978-3-642-17934-1_2",
language = "English",
isbn = "9783642179334",
series = "Studies in Computational Intelligence",
publisher = "Physica-Verlag",
pages = "7--29",
editor = "Halina Kwasnicka and Lakhmi Jain",
booktitle = "Innovations in Intelligent Image Analysis",
}