Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/119121
Title: | Generalized multi-scale stacked sequential learning for multi-class classification |
Author: | Puertas i Prats, Eloi Escalera Guerrero, Sergio Pujol Vila, Oriol |
Keywords: | Algorismes Aprenentatge Algorithms Learning |
Issue Date: | 30-Apr-2015 |
Publisher: | Springer Verlag |
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. |
Note: | Versió postprint del document publicat a: https://doi.org/10.1007/s10044-013-0333-y |
It is part of: | Pattern Analysis and Applications, 2015, vol. 18, num. 2, p. 247-261 |
URI: | http://hdl.handle.net/2445/119121 |
Related resource: | https://doi.org/10.1007/s10044-013-0333-y |
ISSN: | 1433-7541 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
Files in This Item:
File | Description | Size | Format | |
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622017.pdf | 2.21 MB | Adobe PDF | View/Open |
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