Handwritten Digit Recognition by Neural Networks with Single-Layer Training

IEEE Transactions on Neural Networks

Volumn 3, Issue 6, 1992, Pages 962-968

  Knerr, Stefan ^a   Personnaz, Luéon ^a   Dreyfus, Guérard ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION);

FEATURE EXTRACTION; HANDWRITTEN DIGIT RECOGNITION;

NEURAL NETWORKS;

EID: 0026955102     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.165597     Document Type: Article

References (20)

1. M. Minsky and S. Papert, Perceptrons. Cambridge MA: MIT Press, 1969.
2. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing. Cambridge, MA: MIT Press, 1986, vol. 1.
3. K. Hornik, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.
4. S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: A stepwise procedure for building and training a neural network,” NATO Workshop on Neurocomputing, Les Arcs, France (Feb. 1989); in Neurocomputing, F. Fogelman, J. Hérault, Eds. New York: Springer, 1990.
5. S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, vol. 4, pp. 1–58, 1992.
6. W.K. Pratt, Digital Image Processing. New York: Wiley, 1978.
7. H.P. Graf, R. Janow, D. Henderson, and R. Lee, “Reconfigurable neural net chip with 32K connections,” in Neural Information Processing Systems, R.P. Lippmann, J.E. Moody, D.S. Touretzky, Eds. New York: Morgan Kaufmann, 1991.
8. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Wadsworth International, Pacific Grove, CA, 1984.
9. M. Mézard, and J.P. Nadal, “Learning in feedforward layered networks: the tiling algorithm,” J. Phys. A, vol. 22, pp. 2191–2203, 1989.
10. T.D. Sanger, “A tree-structured algorithm for reducing computation in networks with separable basis functions,” Neural Computation, vol. 3, pp. 67–78, 1991.
11. G.Z. Sun, Y.C. Lee, and H.H. Chen, “A novel net that learns sequential decision process,” in Neural Information Processing Systems, D.Z. Anderson, Ed. American Institute of Physics, 1988.
12. P.E. Utgoff, “Perceptron trees: A case study in hybrid concept representations,” Connection Science vol. 1, pp. 377–391, 1989.
13. E. Barnard and D. Casasent, “A comparison between criterion functions for linear classifiers, with an application to neural nets,” IEEE Trans. Syst., Man, Cybern., vol. 19, pp. 1030–1041, 1989.
14. B. Wittner and J. Denker, “Strategies for teaching layered networks classification tasks,” in Neural Information Processing Systems, D.Z. Anderson, Ed. American Institute of Physics, 1988.
15. Y. Chauvin, “Generalization dynamics in LMS trained linear networks,” in Neural Information Processing Systems, R.P. Lippmann, J.E. Moody, and D.S. Touretzky, Eds. New York: Morgan Kaufmann, 1991.
16. Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, pp. 541–551, 1989.
17. Y. LeCun, L.D. Jackel, B. Boser, J.S. Denker, H.P. Graf, I. Guyon, D. Henderson, R.E. Howard, and W. Hubbard, “Handwritten digit recognition: applications of neural net chips and automatic learning,” in Neurocomputing, F. Fogelman, and J. Hérault, Eds. New York: Springer, 1990.
18. B. Boser, E. Säckinger, J. Bromley, Y. LeCun, R. Howard, and L. Jackel, “An analog neural network processor and its application to highspeed character recognition,” in Proc. IJCNN′91, 1991, pp. I-415-I-420.
19. G.L. Martin and J.A. Pittman, “Recognizing hand-printed letters and digits using backpropagation learning,” Neural Computation, vol. 3, pp. 258–267, 1991.
20. P.Y. Alla, G. Saucier, S. Knerr, L. Personnaz, and G. Dreyfus, “Design and implementation of a dedicated neural network for handwritten digit recognition,” in Silicon Architectures for Neural Networks, M.G. Sami, Ed. New York: Elsevier, 1991.