"Kernelized" self-organizing maps for structured data

Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Markus Hagenbuchner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks
Pages19-24
Number of pages6
Publication statusPublished - 1 Dec 2007
Event15th European Symposium on Artificial Neural Networks, ESANN 2007 - Bruges, Belgium
Duration: 25 Apr 200727 Apr 2007

Publication series

NameESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Conference

Conference15th European Symposium on Artificial Neural Networks, ESANN 2007
CountryBelgium
CityBruges
Period25/4/0727/4/07

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Aiolli, F., Da San Martino, G., Sperduti, A., & Hagenbuchner, M. (2007). "Kernelized" self-organizing maps for structured data. In ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks (pp. 19-24). (ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks).