An algorithm to generate radial basis function (RBF)-like nets for classification problems

Asim Roy, Sandeep Govil, Raymond Miranda

Research output: Contribution to journalArticlepeer-review

94 Scopus citations

Abstract

This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems. The method uses linear programming (LP) models to train the RBF-like net. Polynomial time complexity of the method is proven and computational results are provided for many well-known problems. The method can also be implemented as an on-line adaptive algorithm.

Original languageEnglish (US)
Pages (from-to)179-201
Number of pages23
JournalNeural Networks
Volume8
Issue number2
DOIs
StatePublished - 1995

Keywords

  • Classification problems
  • Linear programming models
  • Radial basis function-like nets

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'An algorithm to generate radial basis function (RBF)-like nets for classification problems'. Together they form a unique fingerprint.

Cite this