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Kernelized Self-Organizing Maps for Structured Data

Conference Paper


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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.

UOW Authors


  •   Aiolli, Fabio (external author)
  •   Martino, Giovanni (external author)
  •   Sperduti, Alessandro (external author)
  •   Hagenbuchner, M.

Publication Date


  • 2007

Citation


  • Aiolli, F., Martino, G., Sperduti, A. & Hagenbuchner, M. (2007). Kernelized Self-Organizing Maps for Structured Data. European Symposium on Artificial Neural Networks (pp. 19-24). Belgium: Elsevier.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1676&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/671

Start Page


  • 19

End Page


  • 24

Place Of Publication


  • http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2007-57.pdf

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.

UOW Authors


  •   Aiolli, Fabio (external author)
  •   Martino, Giovanni (external author)
  •   Sperduti, Alessandro (external author)
  •   Hagenbuchner, M.

Publication Date


  • 2007

Citation


  • Aiolli, F., Martino, G., Sperduti, A. & Hagenbuchner, M. (2007). Kernelized Self-Organizing Maps for Structured Data. European Symposium on Artificial Neural Networks (pp. 19-24). Belgium: Elsevier.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1676&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/671

Start Page


  • 19

End Page


  • 24

Place Of Publication


  • http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2007-57.pdf