@Article{EloiPuertas2015,
author="Eloi Puertas
and Sergio Escalera
and Oriol Pujol",
title="Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification",
journal="Pattern Analysis and Applications",
year="2015",
publisher="Springer-Verlag",
volume="18",
number="2",
pages="247--261",
optkeywords="Stacked sequential learning",
optkeywords="Multi-scale",
optkeywords="Error-correct output codes (ECOC)",
optkeywords="Contextual classification",
abstract="In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2251), last updated on Mon, 20 Apr 2015 10:59:51 +0200",
issn="1433-7541",
doi="10.1007/s10044-013-0333-y",
opturl="http://link.springer.com/article/10.1007\%2Fs10044-013-0333-y",
file=":http://refbase.cvc.uab.es/files/PEP2013.pdf:PDF"
}