Support vector machines over a discrete structure: a kernel for qualitative orders of magnitude spaces

N Agell, X Rovira, M Sanchez, F Prats

Research output: Indexed journal article Meeting Abstractpeer-review

Abstract

This paper is within the domain of the study of learning algorithms based on kernels, precisely of the Support Vector Machines. A kernel is constructed over the discrete structure of absolute orders of magnitudes spaces. This kernel is based on an explicit function, defined from the space of k-tuples of qualitative labels to a feature space, which captures the remoteness between the components of the patterns by using certain weights exponentially. A simple example that allows interpreting the kernel in terms of proximity of the patterns is presented.
Original languageEnglish
Pages (from-to)267-275
Number of pages9
JournalFrontiers in Artificial Intelligence and Applications
Volume100
Publication statusPublished - 2003

Keywords

  • Learning algorithms
  • Orders of magnitude reasoning
  • Support vector machines

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