"Kernelized" Self-Organizing Maps for Structured Data
Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Markus Hagenbuchner · 2007
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. 1