Premature Saturation in Backpropagation Networks: Mechanism and Necessary Conditions-Reference-Cited by-同舟云学术

Premature Saturation in Backpropagation Networks: Mechanism and Necessary Conditions

Published:1997-06 Issue:4 Volume:10 Page:721-735
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Vitela Javier E.,Reifman Jaques

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference12 articles.

1. Balakrishnan, K., & Honavar, V. (1992). Improving convergence of back-propagation by handling flat-spots in the output layer. In I. Aleksander & J. Taylor (Eds.), Proceedings of the International Conference on Artificial Neural Networks: Vol. 2 (pp. 1003–1009). Elsevier Science: North-Holland.

2. Chen, J.R., & Mars, P. (1990). Stepsize variation methods for accelerating the back-propagation algorithm. In M. Caudill (Ed.), Proceedings of the International Joint Conference on Neural Networks: Vol. I (pp. 601–604). Piscataway, NJ: IEEE Neural Networks Council.

3. Dahl, E.D. (1987). Accelerated learning using the generalized delta rule. In M. Caudill & C.B. Butler (Eds.), Proceedings of the International Joint Conference on Neural Networks: Vol. II (pp. 523–530). Piscataway, NJ: SOS Printing.

4. Fahlman, S.E. (1989). Faster-Learning Variations on back-propagation: an empirical study. In D. Touretzky, G. Hinton, & T. Sejnowski (Eds.), Proceedings of the Connectionist Models Summer School (pp. 38–51). San Mateo, CA: Morgan Kaufmann.

5. Franzini, M.A. (1987). Speech recognition with back propagation. In Proceedings of the Ninth Annual Conference of the IEEE Engineering in Medicine and Biology Society: Vol. 33 (pp. 1702–1703). New York, NY: IEEE.

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