An exploration of the uncertainty relation satisfied by BP network learning ability and generalization ability

Science in China, Series F: Information Sciences

Volumn 47, Issue 2, 2004, Pages 137-150

  Li, Zuoyong ^a   Peng, Lihong ^b  

^a CHENGDU UNIVERSITY OF INFORMATION TECHNOLOGY   (China)

^b XIAMEN UNIVERSITY   (China)

Author keywords

BP network; Generalization ability; Learning ability; Network structure optimization; Overfit relation

Indexed keywords

EID: 24944472658     PISSN: 10092757     EISSN: None     Source Type: Journal    
DOI: 10.1360/02yf0331     Document Type: Article

References (20)

1. Baum, E. B., Haussler, D., What size net gives valid generalization? NIPSI, 1989, San Mateo, CA, 81-90.
2. Moody, J. E., The effective number of parameters: An analysis of generalization and regularization in nonlinear learning system, NIPS 4,1992, San Mateo, CA, 847-854.
3. Barron, A. R., Approximation and estimation bounds for artificial neural networks, Machine Learning, 1994, (14): 115-133.
4. Geman, S., Neural networks and bias/variance dilemma. Neural Computation, 1992, (4): 1-58.
5. Cataltepe, Z., Abu-mostafa, Y. S., Magdon-Ismail, M., No free lunch for early stopping, Neural Computation, 1999, (11): 995-1009.
6. Amari, S., Murata, N., Muller, K. R. et al., A symptotic statistical theory of overtraining and cross-validation, IEEE Trans. Neural Networks, 1997, 8(5): 985-996.
7. Baldi, P., Temporal evolution of generalization during learning in linear networks, Neural Computation, 1991, (3):589-603.
8. Partridge, D., Network generalization differences quantified, Neural Networks, 1996, 9(2): 263-271.
9. Zhang Hongbin, The problem of the number of samples for training multiple-layered network. Automation Academic Journal, 1993, 19(1): 71-77.
10. Wei Haikun, Xu Sixin, Song Wenzhong, The generalization theory and generalization method of neural network, Automation Academic Journal, 2001, 27(6): 806-815.
11. Yan Pingfan, The capacity, learning and computational complexity of artificial neural network, Electronics Academic Journal, 1995, 23(4): 63-67.
12. Wang Hui, He Xingui, Research on the improvement of BP network generalization ability. System Engineering and Electronic Technology, 2001, 23(3): 85-87, 101.
13. Jiang Xuejun, Tang Huanwen, System analysis of the generalization ability of feed forward neural network, System Engineering Theory and Practice, 2000, 20(8): 38-40.
14. Peng Hanchuan, Gan Qiang, Wei Yu, Some practical methods for improving the extension ability of feed forward neural network, Acta Electronica Sinica, 1998, 26(4): 116-119.
15. Zha Youliang, Information uncertainty principle, Chinese Science Bulletin, 1989, 34(1): 86-87.
16. Shi Neng, Multiple Analytical Methods in Meteorological Statistics and Forecasting, Beijing: Meteorological Press, 1992, 30-33.
17. Li Jianping, Uncertainty principle and its implications and inspirations. Journal of Chinese Academy of Sciences, 2000, 15(6): 428-430.
18. Li Jianping, Zeng Qingcun, Qiu Jifan, Calculation uncertainty principle of non-linear differential equation- II. Theoretical analysis, Science in China, Vol. E, 2000, 30(6): 550-567.
19. Yan Pingfan, Uncertainty principle and neural network used in signal restoration, Signal Processing, 1991, 7(2): 71-76.
20. Zhang Liming, Model of Artificial Neural Network and Its Application. Shanghai: Fudan University Press, 1993, 46.