Abstract
We propose herein a neural network based on curved kernels constituing an anisotropic family of functions and a learning rule to automatically tune the number of needed kernels to the frequency of the data in the input space. The model has been tested on two case studies of approximation problems known to be difficult and gave good results in comparision with traditional radial basis function (RBF) netwoks. Those examples illustrate the fact that curved kernels can locally adapt themselves to match with the observation space regularity.
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References
Darken, C., Moody, J.: Fast learning in networks of locally-tuned processing units. Neural Computation, 1(2):281–294, 1989.
Poggio, T., Girosi, F.: Networks for approximation and learning. Proc. of the IEEE, Vol 78, No 9, September, pp. 1481–1497, 1990.
Benam, M., Tomasini, L.: Competitive and self-organizing algorithms based on the minimization of an information criterion. International Conference on Artificial Neural Networks, 1991.
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© 2001 Springer-Verlag Berlin Heidelberg
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Bourret, P., Pelletier, B. (2001). Curved Kernel Neural Network for Functions Approximation. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_9
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DOI: https://doi.org/10.1007/3-540-45723-2_9
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