2 November 2000 Analysis of decision boundaries of radial basis function neural networks
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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.
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Eunsuk Jung, Eunsuk Jung, Chulhee Lee, Chulhee Lee, } "Analysis of decision boundaries of radial basis function neural networks", Proc. SPIE 4113, Algorithms and Systems for Optical Information Processing IV, (2 November 2000); doi: 10.1117/12.405843; https://doi.org/10.1117/12.405843
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