Analysis of decision boundaries of radial basis function neural networks

Eunsuk Jung, Chulhee Lee

Research output: Contribution to journalConference articlepeer-review


In this paper, we analyze decision boundaries of radial basis function (RBF) neural networks when the RBF neural networks are used as a classifier. We divide the working mechanism of the neural network into two parts: dimension expansion by hidden neurons and linear decision boundary formation by output neurons. First, we investigate the dimension expansion from the input space to the hidden neuron space and then address several properties of decision boundaries in the hidden neuron space that is defined by the outputs of the hidden neurons. Finally, we present a thorough analysis how the number of hidden neurons influences decision boundaries in the input space with illustrations, providing a helpful insight into how RBF networks define complex decision boundaries.

Original languageEnglish
Pages (from-to)134-142
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 2000
EventAlgorithms and Systems for Optical Information Processing IV - San Diego, CA, USA
Duration: 2000 Aug 12000 Aug 2

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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