Intersection and signed-intersection kernels for intervals

Francisco J. Ruiz, Cecilio Angulo, Núria Agell

Research output: Book chapterConference contributionpeer-review

Abstract

In this paper two kernels for interval data based on the intersection operation are introduced. On the one hand, it is demonstrated that the intersection length of two intervals is a positive definite (PD) kernel. On the other hand, a signed variant of this kernel, which also permits discriminating between disjoint intervals, is demonstrated to be a conditionally positive definite (CPD) kernel. The potentiality and performance of the two kernels presented when applying them to learning machine techniques based on kernel methods are shown by considering three different examples involving interval data.

Original languageEnglish
Title of host publicationArtificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
PublisherIOS Press
Pages262-270
Number of pages9
Edition1
ISBN (Print)9781586039257
DOIs
Publication statusPublished - 2008

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume184
ISSN (Print)0922-6389

Keywords

  • Qualitative Reasoning
  • interval analysis
  • kernel methods

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