TY  - JOUR
AU  - Eloi Puertas
AU  - Sergio Escalera
AU  - Oriol Pujol
PY  - 2015//
TI  - Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification
T2  - PAA
JO  - Pattern Analysis and Applications
SP  - 247
EP  - 261
VL  - 18
IS  - 2
PB  - Springer-Verlag
KW  - Stacked sequential learning
KW  - Multi-scale
KW  - Error-correct output codes (ECOC)
KW  - Contextual classification
N2  - 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.
SN  - 1433-7541
UR  - http://link.springer.com/article/10.1007%2Fs10044-013-0333-y
L1  - http://refbase.cvc.uab.es/files/PEP2013.pdf
UR  - http://dx.doi.org/10.1007/s10044-013-0333-y
N1  - HuPBA;MILAB
ID  - Eloi Puertas2015
ER  -