Extending tree kernels with topological information

Fabio Aiolli, Giovanni Martino, Alessandro Sperduti

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

8 Citations (Scopus)

Abstract

The definition of appropriate kernel functions is crucial for the performance of a kernel method. In many of the state-of-the-art kernels for trees, matching substructures are considered independently from their position within the trees. However, when a match happens in similar positions, more strength could reasonably be given to it. Here, we give a systematic way to enrich a large class of tree kernels with this kind of information without affecting, in almost all cases, the worst case computational complexity. Experimental results show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages142-149
Number of pages8
Volume6791 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo
Duration: 14 Jun 201117 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6791 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Artificial Neural Networks, ICANN 2011
CityEspoo
Period14/6/1117/6/11

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Keywords

  • kernel methods
  • machine learning
  • tree kernels

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Aiolli, F., Martino, G., & Sperduti, A. (2011). Extending tree kernels with topological information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6791 LNCS, pp. 142-149). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6791 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21735-7_18