Abstract
A hybrid architecture that includes Radial Basis Functions (RBF) and projection based hidden units is introduced together with a simple gradient based training algorithm. Classification and regression results are demonstrated on various data sets and compared with several variants of RBF networks. In particular, best classification results are achieved on the vowel classification data [1].
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D. H. Deterding. Speaker Normalisation for Automatic Speech Recognition. PhD thesis, University of Cambridge, 1989.
D. L. Donoho and I. M. Johnstone. Projection-based approximation and a duality with kernel methods. Annals of Statistics, 17:58–106, 1989.
R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley, New York, 1973.
G. W. Flake. Square unit augmented, radially extended, multilayer percpetrons. In G. B. Orr and K. Müller, editors, Neural Networks: Tricks of the Trade, pages 145–163. Springer, 1998.
J. H. Friedman. Mutltivariate adaptive regression splines. The Annals of Statistics, 19:1–141, 1991.
T. Hastie and R. Tibshirani. Generalized additive models. Statistical Science, 1:297–318, 1986.
T. Hastie and R. Tibshirani. Generalized Additive Models. Chapman and Hall, London, 1990.
S. Lane, D. Handelman, J. Gelfand, and M. Flax. Function approximation using multi-layered neural networks and b-spline receptive fields. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems, volume 3, pages 684–693, San Mateo, CA, 1991. Morgan Kaufmann.
Y. C. Lee, G. Doolen, H. H. Chen, G. Z.Sun, T. Maxwell, H.Y. Lee, and C. L. Giles. Machine learning using higher order correlation networks. Physica D, 22:276–306, 1986.
D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4(3):415–447, 1992.
M. J. Orr. Introduction to Radial Basis Function networks. Technical report, 1996. http://www.anc.ed.ac.uk/~mjo/rbf.html.
M. J. Orr. Recent advances in Radial Basis Function networks. Technical report http://www.anc.ed.ac.uk/~mjo/papers/recad.ps.gz 1999.
M. J. Orr, J. Hallman, K. Takezawa, A. Murray, S. Ninomiya, M. Oide, and T. Leonard. Combining regression trees and radial basis functions. Division of informatics, Edinburgh University, 1999. Submitted to IJNS.
Gorman R. P. and Sejnowski T. J. Analysis of hidden units in a layered network trained to classify sonar targets. Neural Network, pages 75–89, 1988. Vol. 1.
A. J. Robinson. Dynamic Error Propogation Networks. PhD thesis, University of Cambridge, 1989.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, pages 318–362. MIT Press, Cambridge, MA, 1986.
M. Schetzen. The Volterra and Wiener Theories Of Nonlinear Systems. John Wiley and Sons, New York, 1980.
C. J. Stone. The dimensionality reduction principle for generalized additive models. The Annals of Statistics, 14:590–606, 1986.
V. Volterra. Theory of Functional and of Integro-differential Equations. Dover, 1959.
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Cohen, S., Intrator, N. (2000). A hybrid projection based and radial basis function architecture. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_14
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DOI: https://doi.org/10.1007/3-540-45014-9_14
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