On the power of sigmoid neural networks

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

On the power of sigmoid neural networks

Full text

Pdf (690 KB)

Source

Annual Workshop on Computational Learning Theory archive
Proceedings of the sixth annual conference on Computational learning theory table of contents

Santa Cruz, California, United States

Pages: 137 - 143

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Joe Kilian
Hava T. Siegelmann

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 21, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168321 What is a DOI?

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	Cleeremans A., D. Servan-Schreiber, and J. McClelland, "Finite State Automata and Simple Recurrent Recurrent Networks", Neural Computation, vol 1, No. 3, p. 372 (1989).
	2	Elman J.L., "Finding Structure in Time", Cognitive Science, vol 14, p. 179 (1990).
	3	C. L. Giles , C. B. Miller , D. Chen , H. H. Chen , G. Z. Sun , Y. C. Lee, Learning and extracting finite state automata with second-order recurrent neural networks, Neural Computation, v.4 n.3, p.393-405, May 1992 [doi>10.1162/neco.1992.4.3.393]
	4	John E. Hopcroft , Jeffrey D. Ullman, Introduction To Automata Theory, Languages, And Computation, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1990
	5	Koiran, P., Universal Neural Networks, Manuscript.
	6	W.S. McCulloch, W. Pitts, "A logical calculus of the ideas immanent in nervous activity," Bull. Math. Biophys. 5(1943): 115-133.
	7	Marvin Lee Minsky, Computation: Finite and Infinite Machines, Prentice-Hall, Inc., Upper Saddle River, NJ, 1967
	8	Pollack J. B., On Connectionist Models of Natural Language Processing, Ph.D. Dissertation, Computer Science Dept, Univ. of Illinois, Urbans, 1987.
	9	Pollack J.B., "The Induction of Dynamical Recognizers", Tech Report 90-JP-Automata, Dept of Computer and Information Science, Ohio State U. (1990).
	10	Siegelmann, H. T. and E. D. Sontag, "Turing Computability with Neural Networks" Appl. Math. Left. 4:6, November 1991.
	11	Hava T. Siegelmann , Eduardo D. Sontag, On the computational power of neural nets, Proceedings of the fifth annual workshop on Computational learning theory, p.440-449, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130432]
	12	Siegelmann, H. T.~ and E. D. Sontag, "Neural networks with Real Weights: Analog Computational Complexity" --journal submission.
	13	Williams R.J., and D. Zipser, A Learning Algorithm for Continually Running Fully Recurrent Neural Networks, Neural Computation, Vol. 1, No. 2, p.270, (1989).