Abstract
The RBF network is commonly used for classification and function approximation. The center and radius of the activation function of neurons is an important parameter to be found before the network training. This paper presents a method based on computational geometry to find these coefficients without any parameters provided by the user. The method is compared with a SVM and experimental results showed that our approach is promising.
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Torres, L.C.B., Lemos, A.P., Castro, C.L., Braga, A.P. (2014). A Geometrical Approach for Parameter Selection of Radial Basis Functions Networks. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_67
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DOI: https://doi.org/10.1007/978-3-319-11179-7_67
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-11179-7
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