Publication Details

This conference paper was originally published as Aiolli, F, Da San Martino, G, Sperduti, A and Hagenbuchner, M, "Kernelized” Self-Organizing Maps for Structured Data, European Symposium on Artificial Neural Networks, Bruges, Belgium, 25-27 April 2007, 19-24. Original conference information available here


The suitability of the well known kernels for trees, and the lesser known Self- Organizing Map for Structures for categorization tasks on structured data is investigated in this paper. It is shown that a suitable combination of the two approaches, by defining new kernels on the activation map of a Self-Organizing Map for Structures, can result in a system that is significantly more accurate for categorization tasks on structured data. The effectiveness of the proposed approach is demonstrated experimentally on a relatively large corpus of XML formatted data.