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Lowe, David
Languages: English
Types: Article
Subjects:
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1 Broomhead, D.S. and Lowe, D. (1988) Multi-variable functional interpolation and adaptive networks, Complex Syst., vol. 2, pp. 321- 355.
    • 2 Buhmann, M.D. (2003) Radial Basis Functions: Theory and Implementations, Cambridge University Press.
    • 3 Wu, Y., Wang, H., Zhang, B. and Du, K.-L. (2012) Using radial basis function networks for function approximation and classification, ISRN Appl. Math., vol. 2012, no. 324194, 34 pages.
    • 4 Powell, M.J.D. (2007) A view of algorithms for optimization without derivatives, Math. Today, vol. 43, no. 5, pp. 170-174.
    • 5 www.aston.ac.uk/eas/research/groups/ncrg/resources/ netlab/overview-and-examples/
    • 6 Powell, M.J.D. (1990) The theory of radial basis function approximation in 1990, in Advances in Numerical Analysis, vol. II, Wavelets, Subdivision Algorithms, and Radial Basis Functions, ed. Will Light, Oxford Science Publications, Clarendon Press, pp. 105-210.
    • 7 Micchelli, C.A. (1986) Interpolation of scattered data: distance matrices and conditionally positive definite functions, Constructive Approximation, vol. 2, pp. 11-22.
    • 8 Park, J. and Sandberg, I.W. (1991) Universal approximation using radial basis function networks, Neural Comput., vol. 3, pp. 246-257.
    • 9 Rice, I. and Lowe, D. (2014) Deep layer radial basis function networks for classification, 10th International Conference on Mathematics in Signal Processing, Institute of Mathematics and its Applications.
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