Consolidation Kernel
Résumé
This paper introduces a new kernel for pattern classification. The consolidation kernel is designed to deal with a topological difficulty: a data set where some of the clouds of points associated with the different categories are parted in multiple clusters, possibly distant one from the other. It brings together such clusters. It is incorporated in a multi-class support vector machine. A comparative experimental study highlights its appealing properties.
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