Abstract
The well-known Multi-Layered Perceptron has gained power thanks to the Back Propagation Algorithm. The difficulty which still subsists is its time-wasting. In fact, the learning process can be improved by using the Grow-And-Learn (GAL) algorithm. In this paper, we present such a hybrid system: the cooperation between GAL and MLP networks. The obtained system is more rapid and more efficient than the classic Back Propagation which computes on the MLP.
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References
Y. L. Cun, “Generalization and network design strategies,” technical report crg-tr-89-4, University of Toronto, 1989.
N. Ohnishi, A. Okamoto, and N. Sugie, “Selective presentation of learning samples for efficient learning in multi-layered perceptron,” Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 278–289, Jan. 1990.
E. Alpaydin, “Grow-And-Learn: an incremental method for category learning,” Proceedings of the International Conference on Neural Networks, pp. 761–764, July 1990.
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© 1995 Springer-Verlag Berlin Heidelberg
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Cherruel, G., Solaiman, B., Autret, Y. (1995). Efficient learning in Multi-Layered Perceptron using the Grow-And-Learn algorithm. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_34
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DOI: https://doi.org/10.1007/3-540-60428-6_34
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Online ISBN: 978-3-540-45595-0
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