Decision trees can initialize radial-basis function networks

Research output: Contribution to journalArticlepeer-review

94 Scopus citations


Successful implementations of radial-basis function (RBF) networks for classification tasks must deal with architectural issues, the burden of irrelevant attributes, scaling, and some other problems. This paper addresses these issues by initializing RBF networks with decision trees that define relatively pure regions in the instance space; each of these regions then determines one basis function. The resulting network is compact, easy to induce, and has favorable classification accuracy.

Original languageEnglish (US)
Pages (from-to)813-821
Number of pages9
JournalIEEE Transactions on Neural Networks
Issue number5
StatePublished - Dec 1 1998
Externally publishedYes


  • Decision trees
  • Neural networks
  • Pattern recognition
  • Radial-basis functions

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture


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