Exploiting the ODD framework to define a novel effective graph kernel

Giovanni Martino, Nicoló Navarin, Alessandro Sperduti

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

8 Citations (Scopus)

Abstract

In this paper, we show how the Ordered Decomposition DAGs kernel framework, a framework that allows the definition of graph kernels from tree kernels, allows to easily define new state-of-the-art graph kernels. Here we consider a quite fast graph kernel based on the Subtree kernel (ST), and we improve it by increasing its expressivity by adding new features involving partial tree features. While the worst-case complexity of the new obtained graph kernel does not increase, its effectiveness is improved, as shown on several chemical datasets, reaching state-of-the-art performances.

Original languageEnglish
Title of host publication23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings
Publisheri6doc.com publication
Pages219-224
Number of pages6
ISBN (Electronic)9782875870148
Publication statusPublished - 2015
Event23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Bruges, Belgium
Duration: 22 Apr 201524 Apr 2015

Other

Other23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015
CountryBelgium
CityBruges
Period22/4/1524/4/15

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Martino, G., Navarin, N., & Sperduti, A. (2015). Exploiting the ODD framework to define a novel effective graph kernel. In 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings (pp. 219-224). i6doc.com publication.