Kernels for the Relevance Vector Machine - An empirical study

David Ben-Shimon, Armin Shmilovici

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. Experiments with the Matérn kernel indicate that the kernel choice has a significant impact on the sparsity of the solution. Furthermore, not every kernel is suitable for the RVM. Our experiments indicate that the Matérn kernel of order 3 is a good initial choice for many types of data.

Original languageEnglish
Title of host publicationAdvances in Web Intelligence and Data Mining
EditorsMark Last, Piotr Szczepaniak, Piotr Szczepaniak, Zeev Vlvolkov, Abraham Kandel
Pages253-263
Number of pages11
DOIs
StatePublished - 27 Sep 2006

Publication series

NameStudies in Computational Intelligence
Volume23
ISSN (Print)1860-949X

Keywords

  • Kernel Regression
  • Machine learning
  • Relevance vector machine
  • matérn kernel

ASJC Scopus subject areas

  • Artificial Intelligence

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