On the computational power of limited precision weights neural networks in classification problems: How to calculate the weight range so that a solution will exist

Author:

Draghici Sorin

Publisher

Springer Berlin Heidelberg

Reference28 articles.

1. V. Beiu, S. Draghici, and H. E. Makaruk. On limited fan-in optimal neural networks. Technical Report LA-UR-97-2873, Los Alamos National Laboratory, 1997.

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3. R. Coggins, M. Jabri, and Wattle. A trainable gain analogue vlsi neural network. In Advances in Neural Information Processing Systems, NIPS’93, volume 6, pages 874–881. Morgan Kaufman, 1994.

4. S. Draghici. On the possibilities of the limited precision weights neural networks in classification problems. In J. Mira, R. Moreno-Diaz, and J. Cabestany, editors, Biological and Artificial Computation: From Neuroscience to Technology, Lecture Notes in Computer Science, pages 753–762. Springer-Verlag, 1997.

5. S. Draghici. On VLSI-optimal constructive algorithms for classification problems. In Proc. of EIS’98 International Symposium on Engineering of Intelligent Systems, pages 456–462. ICSC Academic Press, 1998.

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