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Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks
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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: 144 - 150  
Year of Publication: 1993
ISBN:0-89791-611-5
Author
Peter L. Bartlett
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): 16,   Downloads (12 Months): 30,   Citation Count: 3
Additional Information:

references   cited by   index terms   collaborative colleagues  

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DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168322
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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
L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984  [doi>10.1145/1968.1972]
2
Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989  [doi>10.1145/76359.76371]
 
3
Eric B. Baum , David Haussler, What size net gives valid generalization?, Neural Computation, v.1 n.1, p.151-160, Spring 1990  [doi>10.1162/neco.1989.1.1.151]
 
4
Eric B. Baum, On the capabilities of multilayer perceptrons, Journal of Complexity, v.4 n.3, p.193-215, Sept. 1988  [doi>10.1016/0885-064X(88)90020-9]
 
5
P L. Bartlett, "Vapnik-Chervonenkis dimension bounds for two- and three-layer networks," Neural Computation, 5, no. 3, pp. 353-355, 1993.
 
6
A. P. Street, Combmatorics: .4 First Course St Pierre, Canada, Charles Babbage Research Centre. 1982.
 
7
T. Cover, "Geometrical and statistical propertms of systems of linear inequalitms with applications m pattern recognition," IEEE Transactions on Electromc Computers, EC-14, pp. 326-334, 1965.
 
8
W. Maass, "Bounds for the computational power and learning complexity of analog neural nets," Technis~ che Universitaet Graz, (extended abstract), 1992.

CITED BY  3
Wee Sun Lee , Peter L. Bartlett , Robert C. Williamson, Lower bounds on the VC-dimension of smoothly parametrized function classes, Proceedings of the seventh annual conference on Computational learning theory, p.362-367, July 12-15, 1994, New Brunswick, New Jersey, United States
Edward R. Dougherty , Junior Barrera, Pattern Recognition Theory in Nonlinear Signal Processing, Journal of Mathematical Imaging and Vision, v.16 n.3, p.181-197, May 2002
Jonathan Baxter, A model of inductive bias learning, Journal of Artificial Intelligence Research, v.12 n.1, p.149-198, February 2000

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets

Additional Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.2 Modes of Computation
          Subjects: Probabilistic computation

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Pattern analysis


General Terms:
Algorithms, Theory, Verification

Collaborative Colleagues:
Peter L. Bartlett: colleagues

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