Bibliography

Compiani et al., 1989

Compiani, M., Montanari, D., Serra, R., and Valastro, G. (1989).
Classifier systems and neural networks.
In Caianello, E. R., editor, 1st Workshop on Parallel Architectures and Neural Nets.

Gherrity, 1989

Gherrity, M. (1989).
A learning algorithm for analog fully recurrent neural networks.
In IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 1, pages 643-644.

Grossberg, 1976

Grossberg, S. (1976).
Adaptive pattern classification and universal recoding, 1: Parallel development and coding of neural feature detectors.
Biological Cybernetics, 23:187-202.

Holland, 1985

Holland, J. H. (1985).
Properties of the bucket brigade.
In Proceedings of an International Conference on Genetic Algorithms. Hillsdale, NJ.

Kohonen, 1988

Kohonen, T. (1988).
Self-Organization and Associative Memory.
Springer, second edition.

Pearlmutter, 1988

Pearlmutter, B. A. (1988).
Learning state space trajectories in recurrent neural networks.
Technical report, Dept. of Comp. Sci., Carnegie-Mellon Univ., Pittsburgh.

Robinson and Fallside, 1987

Robinson, A. J. and Fallside, F. (1987).
Static and dynamic error propagation networks with application to speech coding.
Proceedings of Neural Information Processing Systems, American Institute of Physics.

Rohwer, 1989

Rohwer, R. (1989).
The `moving targets' training method.
In Kindermann, J. and Linden, A., editors, Proceedings of `Distributed Adaptive Neural Information Processing', St.Augustin, 24.-25.5,. Oldenbourg.

Rumelhart and Zipser, 1986

Rumelhart, D. E. and Zipser, D. (1986).
Feature discovery by competitive learning.
In Parallel Distributed Processing, pages 151-193. MIT Press.

Samuel, 1959

Samuel, A. L. (1959).
Some studies in machine learning using the game of checkers.
IBM Journal on Research and Development, 3:210-229.

Schmidhuber, 1989

Schmidhuber, J. H. (1989).
The neural bucket brigade.
In Pfeifer, R., Schreter, Z., Fogelman, Z., and Steels, L., editors, Connectionism in Perspective, pages 439-446. Amsterdam: Elsevier, North-Holland.

Schmidhuber, 1990a

Schmidhuber, J. H. (1990a).
Networks adjusting networks.
In Kindermann, J. and Linden, A., editors, Proceedings of `Distributed Adaptive Neural Information Processing', St.Augustin, 24.-25.5. 1989, pages 197-208. Oldenbourg.
In November 1990 a revised and extended version appeared as FKI-Report FKI-125-90 (revised) at the Institut für Informatik, Technische Universität München.

Schmidhuber, 1990b

Schmidhuber, J. H. (1990b).
Recurrent networks adjusted by adaptive critics.
In Proc. IEEE/INNS International Joint Conference on Neural Networks, Washington, D. C., volume 1, pages 719-722.

Sutton, 1988

Sutton, R. S. (1988).
Learning to predict by the methods of temporal differences.
Machine Learning, 3:9-44.

Williams and Zipser, 1988

Williams, R. J. and Zipser, D. (1988).
A learning algorithm for continually running fully recurrent networks.
Technical Report ICS Report 8805, Univ. of California, San Diego, La Jolla.

Back to Reinforcement Learning Economy page
Back to Recurrent Neural Networks page