"Kernelized" Self-Organizing Maps for Structured Data

The suitability of the well known kernels for trees, and the lesser known SelfOrganizing 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.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Markus Hagenbuchner
• Keywords: Activation Map, Categorization Tasks, ESANN 2007, Neural Networks, Structured Data