Skip to main content
SpringerLink
Log in

Efficient learning in sparsely connected Boltzmann machines

  • Oral Presentations: Theory Theory II: Learning
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Included in the following conference series:

Abstract

We present a heuristical procedure for efficient estimation of the partition function in the Boltzmann distribution. The resulting speed-up is of immediate relevance for the speed-up of Boltzmann Machine learning rules, especially for networks with a sparse connectivity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D.H. Ackley, G.E. Hinton, and T.J. Sejnowski. A learning algorithm for Boltzmann Machines. Cognitive Science, 9:147–169, 1985.

    Google Scholar 

  2. M.R. Garey and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979.

    Google Scholar 

  3. C. Itzykson and J. Drouffe. Statistical Field Theory. Cambrigde University Press, Cambridge, 1991.

    Google Scholar 

  4. B. Kappen and M.J. Nijman. Radial basis Boltzmann machines and learning with missing values. In Proceedings of the World Congress on Neural Networks, volume I, pages 72–75. Lawrence Erlbaum Associates, Mahwah, NJ, 1995.

    Google Scholar 

  5. R.M. Karp. Reducibility among combinatorial problems. In R.E. Miller and J.W. Thatcher, editors, Complexity of Computer Computations, pages 85–103. Plenum Press, 1972.

    Google Scholar 

  6. S. Kullback. Information theory and statistics. Wiley, N.Y., 1959.

    Google Scholar 

  7. J. Pearl. Probabilistic reasoning using graphs. In B. Bouchon and R.R. Yager, editors, Uncertainty in Knowledge-Based Systems, pages 200–202. Springer-Verlag, 1987.

    Google Scholar 

  8. L. Saul and M. Jordan. Learning in Boltzmann trees. Neural Computation, 6(6):1174–1184, 1994.

    Google Scholar 

  9. D.M.J. Tax and H.J. Kappen. Learning structure with many-take-all networks. In proceedings of ICANN, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Real World Computing Partnership Novel Functions, Dept. of Medical Physics and Biophysics, University of Nijmegen, Geert Grooteplein 21, NL 6525, EZ Nijmegen, The Netherlands

    Marcel J. Nijman & Hilbert J. Kappen

  2. Dutch Foundation for Neural Networks Laboratory Dept. of Medical Physics and Biophysics, University of Nijmegen, Geert Grooteplein 21, NL 6525, EZ Nijmegen, The Netherlands

    Marcel J. Nijman & Hilbert J. Kappen

Authors
  1. Marcel J. Nijman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hilbert J. Kappen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nijman, M.J., Kappen, H.J. (1996). Efficient learning in sparsely connected Boltzmann machines. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_11

Download citation

  • DOI: https://doi.org/10.1007/3-540-61510-5_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation