Skip to main content

Machine learning in communication nets

  • Machine Learning
  • Conference paper
  • First Online:
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 1992)

Abstract

A concept of learning from observations is presented using a combined theory of subjective and objective probability notions in Bayesian networks with tree structure. The principle of learning is applied to communication nets.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literature

  1. Gaines, B.R., Boose, J.H., Machine Learning and Uncertainty Reasoning, London 1990

    Google Scholar 

  2. Lehmann, F., Seising, R., Walther-Klaus, E.: Unterschiedliche Wahrscheinlichkeitsbegriffe in Bayes-Netzwerken, Fortschritte in der Simulationstechnik (ASIM), Bd.1: Simulationstechnik, 6. Symposium in Wien, September 1990, Tagungsband, hrsg. v. F. Breitenecker, I. Troch, P. Kopacek, Braunschweig 1990, pp. 160–164.

    Google Scholar 

  3. Lehmann, F., Seising, R., Walther-Klaus, E.: Analysis of Learning in Bayesian Networks for Fault Management, Fakultätsbericht der Fakulät für Informatik Universität der Bundeswehr München Nr. 9106, Juli 1991

    Google Scholar 

  4. Lehmann, F., Walther-Klaus, E.: Combination of different concepts of probability, in: 6th UK Computer and Telecommunications Performance Engineering Workshop, Bradford, 1990.

    Google Scholar 

  5. Mitchell, T.M., Carbonell, J.G., Michalski, R.S., Machine Learning-A Guide To Current Research, Boston/Dordrecht/Lancaster 1986

    Google Scholar 

  6. Pearl, J.: Fusion, Propagation, and Structuring in Belief Networks. Artificial Intelligence 29, 1986, pp. 241–288.

    Article  MathSciNet  Google Scholar 

  7. Richter, H.: Zur Grundlegung der Wahrscheinlichkeitstheorie. Teil I: Math. Annalen, Bd.125, pp. 129–139, 1953, Teil II: pp. 223–234, Teil III: pp. 335–343, Teil IV: Bd. 126, pp. 362–374, 1953, Teil V: Bd. 128, pp. 305–339, 1954

    Article  Google Scholar 

  8. Richter, H.: Eine einfache Axiomatik der subjektiven Wahrscheinlichkeit. Inst. Nazionale di Alta Matematica Symp. Mathem. Vol IX, 1972, pp. 59–77.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Fevzi Belli Franz Josef Radermacher

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lehmann, F., Seising, R., Walther-Klaus, E. (1992). Machine learning in communication nets. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024982

Download citation

  • DOI: https://doi.org/10.1007/BFb0024982

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55601-5

  • Online ISBN: 978-3-540-47251-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics