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Online learning via congregational gradient descent
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Source Annual Workshop on Computational Learning Theory archive
Proceedings of the eighth annual conference on Computational learning theory table of contents
Santa Cruz, California, United States
Pages: 265 - 272  
Year of Publication: 1995
ISBN:0-89791-723-5
Authors
Kim L. Blackmore  Department of Engineering, Australian National University, Canberra ACT 0200, Australia
Robert C. Williamson  Dept of Engineering, ANU, Canberra ACT 0200, Australia
Iven M. Y. Mareels  Dept of Engineering, ANU, Canberra ACT 0200, Australia
William A. Sethares  Dept of Electrical and Computer Engineering, University of Wisconsin, Madison
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California
Publisher
ACM  New York, NY, USA
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
A. Benveniste and M. Goursat. Blind equalizers. IEEE Transactions on Communications, 32:871-883, August 1984.
 
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K L. Blackmore, R C. Williamson, and I.M.Y. Mareels. Learning nonlinearly parametrized decision regions. Journal of Mathematical Systems, Esttmation, and Control, 1995. To appear.
 
4
J A. Bucklew, TG. Kurtz, and W.A. Sethares. Weak convergence and local stability properties of fixed step size recurslve algorithms. 1EEE Transactions on Infi~rmatton Theory, 39:966-978, 1993.
5
 
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Z. Ding, R.A. Kennedy, B.D.O. Anderson, and C.R. Johnson Jr. Ill-convergence of Godard blind equahzers in data communication systems. IEEE Transactions on Communications, 39(9): 1313-1327, 1991.
 
7
K. Dogancay and R.A. Kennedy. Testing for the convergence of a linear decision directed equalizer, lEE Proc.-Vis. Image Signal Process., 141 (2): 129-136, April 1994.
 
8
K. Dogancay and R.A. Kennedy. Testing output performance in blind adaptation. In Proceediugs of the 33rd IEEE Conference on Dectsto~z and Control, pages 2817-2818, December 1994.
 
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S. Forrest. Genetic algorithms: Principles of natural selection applied to computation. Science, 261:972-978, 13 August 1993.
 
11
M.R. Frater, R.R. Bitmead, and C.R. Johnson Jr. Escape from stable equilibria in blind adaptive equalization. In Proceedings of the 31st IEEE Conference on Decision and Control, pages 1756-1761, December 1992.
 
12
TM. Heskes and B. Kappen. On-hne learning processes in artificial neural networks. In J.G. Taylor, editor, Mathematical Approaches to Neural Networks, pages 199-233. North- Holland, Amsterdam, 1993.
 
13
M.W. Hirsch and S. Smale. Differential Equations, Dwzamical Systems, and Linear Algebra. Academic Press, New York, 1974.
 
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15
C.R. Johnson Jr, S. Dasgupta, and W.A. Sethares. Averaging analysis of local stabfiity of a real constant modulus algorithm adaptive filter. 1EEE Transactions on Acoustics, Speech and Signal Processing, 36(6):900-910, 1988.
 
16
C.-M. Kuan and K. Hornik. Convergence of learning algorithms with constant learning rates. IEEE Transactions o~ Neural Networks, 2(5):484-489, 1991.
 
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18
J A. Sanders and F. Verhulst. Averaging Methods in Nonlinear Dynamical System~. Applied Mathematical Sciences; v. 59. Springer-Verlag, New York, 1985.
 
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Collaborative Colleagues:
Kim L. Blackmore: colleagues
Robert C. Williamson: colleagues
Iven M. Y. Mareels: colleagues
William A. Sethares: colleagues