An algorithm of supervised learning for multilayer neural networks

Neural Computation

Volumn 15, Issue 5, 2003, Pages 1125-1142

  Tang, Zheng ^a   Wang, XuGang ^a   Tamura, Hiroki ^a   Ishii, Masahiro ^a  

^a UNIVERSITY OF TOYAMA   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0042879436     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603765202686     Document Type: Article

References (18)

1. Abunawass, A. M., & Owen, C. B. (1993). A statistical analysis of the effect of noise injection during neural network training. SPIE Proceedings, 1966, 362-371.
2. Amato, S., Apolloni, B., Caporali, G., Madesani, U., & Zanaboni, A. (1991). Simulated annealing approach in backpropagation. Neurocomputing, 3, 207-220.
3. Battiti, R., & Masulli, F. (1990). BFGS optimization for faster and automated supervised learning. In Proceedings of the International Neural Network Conference (pp. 757-760). Paris, France. Dordrecht: Kluwer.
4. Fukuoka, Y., Matsuki, H., Minamitani, H., & Ishida, A. (1998). A modified back-propagation method to avoid false local minima. Neural Networks, 11, 1059-1072.
5. Hanson, S. J. (1991). Behavioral diversity, search and stochastic connectionist systems. In M. Commons, S. Grossberg, & J. Staddon (Eds.), Neural network models of conditioning and action (pp. 295-345). Mahwah, NJ: Erlbaum.
6. Hassoun, M. (1995). Fundamentals of artificial neural networks, Cambridge, MA: MIT Press.
7. Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 282-317). Cambridge, MA: MIT Press.
8. Hjelmås, E., & Munro, P. W. (1999). A comment on the parity problem (Report 7). Gjøvik, Norway: Gjøvik College.
9. Ingman, D., & Merlis, Y. (1991). Local minimization escape using thermodynamic properties of neural networks. Neural Networks, 4, 395-404.
10. Kollias, S., & Anastassiou, D. (1989). An adaptive least squares algorithm for efficient training of artificial neural networks. IEEE Transactions on Circuits and Systems, 36, 1092-1101.
11. Kramer, A. H., & Sangiovanni-Vincentelli, A. (1988). Efficient parallel learning algorithms for neural networks. In D. S. Touretzky (Ed.), Advances in neural information processing systems, 1 (pp. 75-89). San Mateo, CA: Morgan Kaufmann.
12. Owen, C. B., & Abunawass, A. M. (1993). Application of simulated annealing to the backpropagation model improves convergence, SPIE Proceedings, 1966, 269-276.
13. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by back-propagating errors. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 318-362). Cambridge, MA: MIT Press.
14. Shepherd, A. J. (1997). Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptrons. London: Springer-Verlag.
15. Sigillito, V. G., Wing, S. P., Hutton, L. V., & Baker, K. B. (1989). Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10, 262-266.
16. Von Lehmen, A., Paek, E.G., Liao, P. F., Marrakchi, A., & Patel, J. S. (1988). Factors influencing learning by backpropagation. In Proceedings of IEEE International Conference on Neural Networks (pp. 335-341). New York: IEEE Press.
17. Wang, C., & Principe, J. C. (1999). Training neural networks with additive noise in the desired signal. IEEE Transactions on Neural Networks, 10, 1511-1517.
18. Watrous, R. L. (1987). Learning algorithms for connectionist networks: Applied gradient methods of nonlinear optimization. In Proceedings of the International Conference on Neural Networks (pp. 619-627). New York: IEEE Press.