Controlling Hidden Layer Capacity Through Lateral Connections

Neural Computation

Volumn 9, Issue 6, 1997, Pages 1381-1402

  Agyepong, Kwabena ^a   Kothari, Ravi ^a  

^a UNIVERSITY OF CINCINNATI   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000255165     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.6.1381     Document Type: Article

References (19)

1. Baum, E. B., and Haussler, D. (1989). What sized net gives valid generalization? Neural Computation 1(1):151-160.
2. Bishop, C. M. (1995). Neural networks for pattern recognition. NY: Oxford University Press.
3. Bose, N. K., & Garga, A. K. (1993). Neural network design using Voronoi diagrams. IEEE Transactions on Neural Networks 4(5):778-787.
4. Chauvin, Y. S. (1989). A back-propagation algorithm with optimal use of hidden units. In D. S. Touretzky, (Ed.), Advances in Neural Information Processing Systems 2 (pp. 642-649). San Mateo, CA: Morgan Kaufmann.
5. Cios, K. J., & Liu, N. (1992). A machine learning method for generation of a neural network architecture: A continuous ID3 algorithm. IEEE Transactions Neural Networks 3(2):280-291.
6. Fahlman, S. E., & Lebiere, C. (1991). The cascade-correlation learning architecture. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 2 (pp. 524-532). San Mateo, CA: Morgan Kaufmann.
7. Gallant, S. I. (1986). Three constructive algorithms for network learning. In Proc. of the Eighth Annual Conference of the Cognitive Science Society (pp. 652-660). Amherst, MA.
8. Hassibi, B., & Stork, D. G. (1993). Second order derivatives for networks pruning: Optimal brain surgeon. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in Neural Information Processing Systems 5 (pp. 164-171). San Mateo, CA: Morgan Kaufmann.
9. Karnin, E. D. (1990). A simple procedure for pruning back propagation trained neural networks. IEEE Transactions on Neural Networks 1(2):239-242.
10. Kothari, R., & Agyepong, K. (1996). On lateral connections in feed-forward neural networks. In Proc. IEEE International Conference on Neural Networks (pp. 13-18).
11. Kwok, T., & Yeung D. (1995). Constructive feedforward neural networks for regression problems: A survey (Tech. Rep. HKUST-CS95-43). Hong Kong University of Science and Technology.
12. LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimal brain damage. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 2 (pp. 598-605). San Mateo, CA: Morgan Kaufmann.
13. Mozer, M. C., & Smolensky, P. (1989). Skeletonization: A technique for trimming the fat from a network via relevance assessment. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 1 (pp. 107-115). San Mateo, CA: Morgan Kaufmann.
14. Nadal, J. P. (1989). Study of a growth algorithm for neural networks. International Journal of Neural Systems 1:55-59.
15. Nowlan, S. J., & G. E. Hinton. (1992). Simplifying neural networks by soft weight sharing. Neural Computation 4(4):473-493.
16. Plaut, D. C., Nowlan, S. J., & Hinton, G. E. (1986). Experiments on learning by back-propagation (Tech. Rep. CMU-CS-86-126). Pittsburgh, PA: Carnegie Mellon Univerisity.
17. Reed, R. (1993). Pruning algorithms - A survey. IEEE Transactions on Neural Networks 4(5):740-747.
18. Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.
19. Weigend, A. S., Rumelhart, D. E., & Huberman, B. A. (1991). Generalization by weight elimination with application to forecasting. In R. P. Lippman, J. E. Moody, & D. S. Touretzky (Eds.), Advances in Neural Information Processing Systems 3 (pp. 875-882). San Mateo, CA: Morgan Kaufmann.