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Practical realization of a radial basis function network for handwritten digit recognition

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

We present a practical realization of a Radial Basis Function Network for handwritten digits recognition task. Inspired from regularization theory and Parzen windows non parametric estimator, Radial Basis Function networks are tested for a classification task. Reduction of the number of hidden nodes which is an important and necessary step to obtain a computationally tractable network is made using an original technique. A comparison is made with the k-nearest neighbour and Parzen windows methods. Results appear better for the network at a much lower computational cost.

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References

  1. Poggio T., Girosi F., Networks for approximation and learning.Proceedings of the IEEE, Vol 78, No. 9,1990.

    Google ScholarĀ 

  2. Girosi F., Poggio T., Networks and the best approximation property. Biological Cybernetic 63, 169ā€“176, 1990.

    Google ScholarĀ 

  3. Richard M. D., Lippman R. P., Neural Networks Classifiers estimate a posteriori Probabilities. Neural Computation, 4, 461ā€“483, 1991.

    Google ScholarĀ 

  4. Lee Y., Handwritten recognition using K Nearest-Neighbour, Radial Basis Function and Backpropagation Neural Networks. Neural Computation, 3, 440ā€“449, 1991.

    Google ScholarĀ 

  5. Moody J., Darken C. J., Fast learning in Networks of locally tuned processing units, Neural Computation, 1, 281ā€“294, 1989.

    Google ScholarĀ 

  6. Ng, Lipmann R.P., A comparative study of the practical characteristics of neural networks and conventional pattern clasifiers, in Neural Information Processing Systems 3, 1991, D.S. Touretzky, ed. Morgan Kaufmann, San Mateo, Ca.

    Google ScholarĀ 

  7. Geman S, Bienenstock E., Boursat R., Neural Networks and the bias variance dilemma, Neural Computation, 4, 1ā€“58, 1992.

    Google ScholarĀ 

  8. Gluksman H. A., Classification of Mixed Font alphabetics by characteristics loci., 1st annual IEEE Computer Conference, 138ā€“141, 1967.

    Google ScholarĀ 

  9. Gaillat G., Berthod M., Panorama des techniques d'extraction de traits caractĆ©ristiques en lecture, optique des caractĆØres, Revue Technique THOMSON-CSF, Vol 11, No 4, 1979.

    Google ScholarĀ 

  10. Specht D. F., Probabilistic Neural Networks, Neural Networks, vol. 3, 109ā€“118, 1990.

    Google ScholarĀ 

  11. Musavi M. T., Ahmed W., Chand K. H., Faris K. B., Hummels D. M., On the Training of Radial Basis Function Classifiers, Neural Networks, Vol 5, 595ā€“605, 1992.

    Google ScholarĀ 

  12. Hausler D., Decision Theoretic Generalization of the PAC Model for Neural Net and Other Learning Applications, Technical Report, UCSC-CRL-91-02, 1991.

    Google ScholarĀ 

  13. White H, Conectionist Non Parametric Regression: Multilayer feedforward Networks can learn Arbitrary Mappings, Neural Networks, Vol. 3, 535ā€“549, 1990.

    Google ScholarĀ 

  14. Devroye L.,Automatic Pattern Recognition: A study of the Probability of Error, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 10, No.4, 1988.

    Google ScholarĀ 

  15. Chiu S.T., Bandwith Selection for Kernel Density Estimation, The Annals of Statistics, vol. 19, no. 4, 1883ā€“1905, 1991.

    Google ScholarĀ 

  16. Somorjai R.L., Ali M.K. An efficient algorithm for estimating dimensionalities, Can. J. Chem. 66, 979, 1988.

    Google ScholarĀ 

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JosƩ Mira Joan Cabestany Alberto Prieto

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Ā© 1993 Springer-Verlag Berlin Heidelberg

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LemariƩ, B. (1993). Practical realization of a radial basis function network for handwritten digit recognition. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_136

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  • DOI: https://doi.org/10.1007/3-540-56798-4_136

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

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