Learning Kernel Label Decompositions for Ordinal Classification Problems

M. Pérez-Ortiz, P. A. Gutiérrez, C. Hervás-Martínez

Abstract

This paper deals with the idea of decomposing ordinal multiclass classification problems when working with kernel methods. The kernel parameters are optimised for each classification subtask in order to better adjust the kernel to the data. More flexible multi-scale Gaussian kernels are considered to increase the goodness of fit of the kernel matrices. Instead of learning independent models for all the subtasks, the optimum convex combination of the kernel matrices is then obtained, leading to a single model able to better discriminate the classes in the feature space. The results of the proposed algorithm shows promising potential for the acquisition of better suited kernels.

References

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Paper Citation


in Harvard Style

Pérez-Ortiz M., A. Gutiérrez P. and Hervás-Martínez C. (2014). Learning Kernel Label Decompositions for Ordinal Classification Problems . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 218-225. DOI: 10.5220/0005079302180225


in Bibtex Style

@conference{ncta14,
author={M. Pérez-Ortiz and P. A. Gutiérrez and C. Hervás-Martínez},
title={Learning Kernel Label Decompositions for Ordinal Classification Problems},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={218-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005079302180225},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Learning Kernel Label Decompositions for Ordinal Classification Problems
SN - 978-989-758-054-3
AU - Pérez-Ortiz M.
AU - A. Gutiérrez P.
AU - Hervás-Martínez C.
PY - 2014
SP - 218
EP - 225
DO - 10.5220/0005079302180225