Abstract
In this paper, to improve the generalization ability of radial basis function networks, we apply the polynomial neural networks as the virtual input variables of radial basis function networks. The parameters of each polynomial neuron are estimated by linear discriminant analysis. In each layer of polynomial neural networks, the polynomial neurons are selected in terms of the objective function of linear discriminant analysis.
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Ahn, T.C., Roh, S.B., Yin, Z.L., Kim, Y.S. (2014). Design of Radial Basis Function Classifier Based on Polynomial Neural Networks. In: Cho, Y., Matson, E. (eds) Soft Computing in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-319-05515-2_10
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DOI: https://doi.org/10.1007/978-3-319-05515-2_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05514-5
Online ISBN: 978-3-319-05515-2
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