Evolving Neural Networks using Moment Method for Handwritten Digit Recognition
This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.
[1] M. K. Hu, "Visual pattern recognition by moment invariants," IRE
Transaction on Information Theory, vol. 8, no. 2, pp. 179-187, 1962.
[2] S. X. Liao and Miroslaw Pawlak, "On image analysis by moments,"
IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no.
3, pp. 254-266, 1996.
[3] H. Qjidaa and L. Redouane, "Robust line fitting in a noisy image by the
method of moments," IEEE Trans. on Pattern Analysis and Machine
Intelligence, vol. 21, no. 11, pp. 1216-1223, 1999.
[4] W. H. schiffmann and K. Mecklenburg, "Genetic Generation of
Backpropagation Trained Neural Networks," Proc. of Parallel
Processing in Neural Systems and Computers(ICNC), Eckmiller R. et al.
(Eds.) pp. 205-208, Elsevier, 1990.
[5] G. Miller P. M. Todd and S. U. Hegde, Designing Neural Networks
using Genetic Algorithms, Proc. Of the third Intern. Conference on
Genetic Algorithms (ICGA), San Mateo (CA), 1989, pp. 379-384.
[6] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
learning applied to document recognition," Proceedings of the IEEE,
vol. 86, no.11, pp. 2278-2324, November 1998.
[7] H. El Fadili, K. Zenkouar and H. Qjidaa, "Lapped Block Image Analysis
Via the Method of Legendre Moments," EURASIP Journal on Applied
Signal Processing, vol. 2003, no.9, pp. 902-913, August 2003.
[8] X. Zhunang, R. M. Haralick, and Y. Zhao, "Maximum entropy image
reconstruction," IEEE Trans. Signal Processing, vol. 39, no. 6, pp.
1478-1480, 1991.
[9] D. Parisi, A.Cangelosi and S. Nolfi, "cell division and migration in a
genotype for neural networks," Network: computation in neural systems,
vol. 5, no. 4, 1994.
[10] D. Whitley, T. Starkweather, and C. Bogart, "Genetic algorithms and
neural networks: optimizing connections and connectivity," Parallel
Computing,vol. 14, pp. 347-361, 1990.
[11] Y. Liu and X. Yao (1996), "A population-based learning algorithm
which learns both architectures and weights of neural networks,"
Chinese Journal of Advanced Software Research (Allerton Press, Inc.,
New York, NY 10011), vol. 3, no. 1, pp. 54-65, 1996.
[1] M. K. Hu, "Visual pattern recognition by moment invariants," IRE
Transaction on Information Theory, vol. 8, no. 2, pp. 179-187, 1962.
[2] S. X. Liao and Miroslaw Pawlak, "On image analysis by moments,"
IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no.
3, pp. 254-266, 1996.
[3] H. Qjidaa and L. Redouane, "Robust line fitting in a noisy image by the
method of moments," IEEE Trans. on Pattern Analysis and Machine
Intelligence, vol. 21, no. 11, pp. 1216-1223, 1999.
[4] W. H. schiffmann and K. Mecklenburg, "Genetic Generation of
Backpropagation Trained Neural Networks," Proc. of Parallel
Processing in Neural Systems and Computers(ICNC), Eckmiller R. et al.
(Eds.) pp. 205-208, Elsevier, 1990.
[5] G. Miller P. M. Todd and S. U. Hegde, Designing Neural Networks
using Genetic Algorithms, Proc. Of the third Intern. Conference on
Genetic Algorithms (ICGA), San Mateo (CA), 1989, pp. 379-384.
[6] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
learning applied to document recognition," Proceedings of the IEEE,
vol. 86, no.11, pp. 2278-2324, November 1998.
[7] H. El Fadili, K. Zenkouar and H. Qjidaa, "Lapped Block Image Analysis
Via the Method of Legendre Moments," EURASIP Journal on Applied
Signal Processing, vol. 2003, no.9, pp. 902-913, August 2003.
[8] X. Zhunang, R. M. Haralick, and Y. Zhao, "Maximum entropy image
reconstruction," IEEE Trans. Signal Processing, vol. 39, no. 6, pp.
1478-1480, 1991.
[9] D. Parisi, A.Cangelosi and S. Nolfi, "cell division and migration in a
genotype for neural networks," Network: computation in neural systems,
vol. 5, no. 4, 1994.
[10] D. Whitley, T. Starkweather, and C. Bogart, "Genetic algorithms and
neural networks: optimizing connections and connectivity," Parallel
Computing,vol. 14, pp. 347-361, 1990.
[11] Y. Liu and X. Yao (1996), "A population-based learning algorithm
which learns both architectures and weights of neural networks,"
Chinese Journal of Advanced Software Research (Allerton Press, Inc.,
New York, NY 10011), vol. 3, no. 1, pp. 54-65, 1996.
@article{"International Journal of Information, Control and Computer Sciences:62945", author = "H. El Fadili and K. Zenkouar and H. Qjidaa", title = "Evolving Neural Networks using Moment Method for Handwritten Digit Recognition", abstract = "This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.", keywords = "Genetic algorithm, Legendre Moments, MEP,
Neural Network.", volume = "1", number = "11", pages = "3699-4", }