Analysis of decision boundaries of radial basis function neural networks

Eunsuk Jung, Chulhee Lee

Research output: Contribution to journalConference article

Abstract

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
Volume4113
DOIs
Publication statusPublished - 2000 Dec 1
EventAlgorithms and Systems for Optical Information Processing IV - San Diego, CA, USA
Duration: 2000 Aug 12000 Aug 2

Fingerprint

Radial Basis Function Neural Network
neurons
Neurons
Neuron
Neural networks
expansion
Radial basis function networks
Radial Basis Function Network
output
Output
classifiers
Divides
Classifiers
Classifier
Neural Networks

All Science Journal Classification (ASJC) codes

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

Cite this

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Analysis of decision boundaries of radial basis function neural networks. / Jung, Eunsuk; Lee, Chulhee.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 4113, 01.12.2000, p. 134-142.

Research output: Contribution to journalConference article

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