The suitability of the well known kernels for trees, and the l esser known Self- Organizing Map for Structures for categorization tasks on structured data is inves- tigated in this paper. It is shown that a suitable combinatio n 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 accur ate for categorization tasks on structured data. The effectiveness of the proposed approach is demon- strated experimentally on a relatively large corpus of XML formatted data.

"Kernelized" Self-Organizing Maps for Structured Data

AIOLLI, FABIO;DA SAN MARTINO G;SPERDUTI, ALESSANDRO
2007

Abstract

The suitability of the well known kernels for trees, and the l esser known Self- Organizing Map for Structures for categorization tasks on structured data is inves- tigated in this paper. It is shown that a suitable combinatio n 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 accur ate for categorization tasks on structured data. The effectiveness of the proposed approach is demon- strated experimentally on a relatively large corpus of XML formatted data.
Proceedings of the 15th European Symposium on Artificial Neural Networks
2930307072
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2469023
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