%0 Journal Article
%T Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification
%A Eloi Puertas
%A Sergio Escalera
%A Oriol Pujol
%J Pattern Analysis and Applications
%D 2015
%V 18
%N 2
%I Springer-Verlag
%@ 1433-7541
%F Eloi Puertas2015
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2251), last updated on Mon, 20 Apr 2015 10:59:51 +0200
%X 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.
%K Stacked sequential learning
%K Multi-scale
%K Error-correct output codes (ECOC)
%K Contextual classification
%U http://link.springer.com/article/10.1007%2Fs10044-013-0333-y
%U http://refbase.cvc.uab.es/files/PEP2013.pdf
%U http://dx.doi.org/10.1007/s10044-013-0333-y
%P 247-261