"Kernalized" self-organizing maps for structured data

Authors

F. Aiolli, University of Padova, Italy
G. Da San Martino, University of Padova, Italy
A. Sperduti, University of Padova, Italy
M. Hagenbuchner, University of WollongongFollow

RIS ID

20587

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

Abstract

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.