Training neural networks with additive noise in the desired signal

IEEE Transactions on Neural Networks

Volumn 10, Issue 6, 1999, Pages 1511-1517

  Wang, Chuan ^a   Principe, Jose C ^b  

^a LUCENT TECHNOLOGIES   (United States)

^b University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; BACKPROPAGATION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

ADDITIVE RANDOM NOISE; BACKPROPAGATION ALGORITHMS;

NEURAL NETWORKS;

EID: 0033333990     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.809097     Document Type: Article

References (28)

1. G. An, "The effects of adding noise during backpropagation training on a generalization performance," Neural Comput., vol. 8, pp. 643-674, 1996.
2. B. Atal, "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification," J. Acoust. Soc. Amer., June 1974.
3. C. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford Univ. Press, 1995.
4. C. Darken, J. Chang, and J. Moody, "Learning rate schedules for faster stochastic gradient search," in IEEE Wrkshp. Neural Networks for Signal Processing, 1992, pp. 15-21.
5. B. de Vries and J. C. Principe, "The gamma model - A new neural model for temporal processing," Neural Networks, vol. 5, no. 4, pp. 565-576, 1992.
6. W. Edmonson, K. Srinivasan, C. Wang, and J. Principe, "A global least square algorithm for adaptive IIR filtering," IEEE Trans. Circuits Syst., vol 45, pp. 379-384, 1997.
7. H. Gish and M. Schmidt, "Text-independent speaker identification," IEEE Signal Processing Mag., Oct. 1994.
8. S. Fahlman, "Fast-learning variations on backpropagation: An empirical study," in Proc. 1988 Connectionist Model Summer School. San Mateo, CA: Morgan Kaufmann.
9. W. Feller, An Introduction to Probability Theory and Its Applications, vol. 1, 2nd ed. New York: Wiley, 1966.
10. Y. Grandvalet and S. Canu, "A comment on noise injection into inputs in back-propagation learning," IEEE Trans. Syst., Man, Cybern., vol. 25, 1995.
11. S. Haykin, Neural Networks - A Comprehensive Foundation. New Yor:, Macmillan, 1994.
12. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the theory of neural computation. Reading, MA: Addison-Wesley, 1991.
13. G. E. Hinton, "Connectionist learning procedure," in Machine Learning: Paradigms and Methods, J. G. Carbonell, Ed. Cambridge, MA: MIT Press, 1989, pp. 185-234.
14. L. Holmstrom and P. Koistinen, "Using additive noise in backpropagation training," IEEE Trans. Neural Networks, vol. 3, 1992.
15. S. Kirkpatrick et al., "Optimization by simulated annealing," Science, vol. 220, pp. 671-680.
16. A. Krogh and J. Hertz, "Generalization in a linear perceptron in the present of noise," J. Phys. A: Math. Gen., vol. 25, 1992, pp. 1135-1147.
17. H. Kushner, "Asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects: Global minimization via Monte Carlo," SIAM J. Appl. Math., vol. 47, no. 1, Feb. 1987.
18. K. Matsuoka, "Noise injection into inputs in back-propagation learning," IEEE Trans. Syst., Man, Cybern., vol. 22, no. 3, 1992.
19. J. C. Principe, J.-M. Kuo, and S. Celebi, "An analysis of the Gamma memory in dynamic neural networks," IEEE Trans. Neural Networks, vol. 5, Mar. 1994.
20. M. Richard and R. P. Lippmann, "Neural network classifiers estimate Bayesian a posteriori probability," Neural Computation, vol. 3, pp. 461-483, 1991.
21. T. Rognvaldsson, "On Langevin updating in multilayer perceptrons," Neural Comput., vol. 6, pp. 916-926, 1994.
22. Rumelhart et al., Parallel Distributed Processing, vol. 1. Cambridge, MA: MIT Press, 1986.
23. R. Reed, R. J. Marks, II, and S. Oh, "Similarities of error regularization signoid gain scaling, target smoothing, and training with jitter," IEEE Trans. Neural Networks, vol. 6, 1995.
24. C. Wang, X. Dongxin, and J. C. Principe, "Speaker verification and identification using Gamma neural networks," in 1997 Int. Joint Conf. Neural Networks, Houston, TX, June 1997.
25. A. R. Webb, "Functional approximation by feedforward networks: A least squares approach to generalization," IEEE Trans. Neural Networks, vol. 5, Mar. 1994.
26. P. Werbos, "Generalization of backpropagation with application to a recurrent gas market model," Neural Networks, vol. 1, pp. 339-356.
27. B. Widrow and M. Hoff, "Adaptive switching circuits," in IRE WESCON Convention Rec., pp. 96-104, 1960.
28. J. S. Garofolo, "Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database," Nat. Inst. Standards Technol., Gaithersburgh, MD, 1988.