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Título:	Generalized multi-scale stacked sequential learning for multi-class classification
Autor/a:	Puertas i Prats, Eloi; Escalera Guerrero, Sergio; Pujol Vila, Oriol
Otros autores:	Universitat de Barcelona
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.
Materia(s):	-Algorismes -Aprenentatge -Algorithms -Learning
Derechos:	(c) Springer Verlag, 2015
Tipo de documento:	Artículo Artículo - Versión aceptada
Editor:	Springer Verlag
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