Learning Kernel Label Decompositions for Ordinal Classification Problems

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Research areas:
Year:
2014
Type of Publication:
In Proceedings
Keywords:
Kernel Learning, Support Vector Machines, Ordinal Classification, Kernel-target Alignment
Authors:
Book title:
6th International Conference on Neural Computation Theory and Applications (NCTA2014)
Pages:
218-225
Organization:
Roma (Italy)
Month:
22th-24th October
ISBN:
978-989-758-054-3
BibTex:
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.
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