An efficient topological distance-based tree kernel

Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Tree kernels proposed in the literature rarely use information about the relative location of the substructures within a tree. As this type of information is orthogonal to the one commonly exploited by tree kernels, the two can be combined to enhance state-of-the-art accuracy of tree kernels. In this brief, our attention is focused on subtree kernels. We describe an efficient algorithm for injecting positional information into a tree kernel and present ways to enlarge its feature space without affecting its worst case complexity. The experimental results on several benchmark datasets are presented showing that our method is able to reach state-of-the-art performances, obtaining in some cases better performance than computationally more demanding tree kernels.

Original languageEnglish
Article number6844015
Pages (from-to)1115-1120
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number5
DOIs
Publication statusPublished - 1 May 2015

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Keywords

  • Kernel methods
  • kernels for structured data
  • learning in structured domains
  • position aware kernels
  • tree kernels.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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