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Generalized multi-scale stacked sequential learning for multi-class classification.
- Source :
- Pattern Analysis & Applications; May2015, Vol. 18 Issue 2, p247-261, 15p
- Publication Year :
- 2015
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- SEQUENTIAL learning
ERROR-correcting codes
ALGORITHMS
DATABASES
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 14337541
- Volume :
- 18
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Pattern Analysis & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 101679618
- Full Text :
- https://doi.org/10.1007/s10044-013-0333-y