Skip to main content

Risk Analysis Using Perceptrons and Quadratic Programming

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1625))

Abstract

A heuristic method (computation of weighted averages) is considered for risk analysis. It is improved upon by utilizing the Perceptron Learning Theorem and Quadratic Programming. Experimental work shows that both techniques give good results, the former one being somewhat more efficient in terms of CPU-time used. In spite of certain theoretical shortcomings it is argued that the familiar paradigm offers considerable potential for practical applications.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barczewski, T.; Rust, H.J.; Weber, R.; Zygan, H.: Vorstellung eines Fuzzy Entscheidungssystems zur Bonitaetsbeurteilung von Unternehmen, in: Fuzzy Technologien bei Finanzdienstleistern, MIT-Management Intelligenter Technologien GmbH, Promenade 9, 52076 Aachen, (1997)

    Google Scholar 

  2. Bishop, C.M. Neural Networks for Pattern Recognition, Oxford University Press, (1995)

    Google Scholar 

  3. Falkowski, B.-J.: On Certain Generalizations of Inner Product Similarity Measures, Journal of the American Society for Information Science, Vol. 49, No. 9, (1998)

    Google Scholar 

  4. Hampson, S.E.; Volper, D.J.: Feature Handling in Learning Algorithms, in: Dynamic Interactions in Neural Networks: Models and Data, Springer, (1988)

    Google Scholar 

  5. Hertz, J.; Krogh, A.; Palmer, R.G.: Introduction to the Theory of Neural Computation, Addison-Wesley, (1991)

    Google Scholar 

  6. Heyder, F.; Zayer, S.: Analyse von Kurszeitreihen mit Kuenstlichen Neuronalen Netzen und Competing Experts, in: Informationssysteme in der Finanzwirtschaft, Springer, (1998), pp. 489–500

    Google Scholar 

  7. Locarek-Junge, H.; Prinzler, R.: Estimating Value-at-Risk Using Neural Networks, in: Informationssysteme in der Finanzwirtschaft, Springer, (1998), pp. 385–397

    Google Scholar 

  8. Luenberger, D.G.: Linear and Nonlinear Programming, Addison-Wesley, (1986)

    Google Scholar 

  9. McLewin, W.: Linear Programming and Applications, Manchester Student Edition, Input-Output Publishing Company, (1980)

    Google Scholar 

  10. Minsky, M.L.; Papert, S.: Perceptrons, MIT Press, 3rd edition, (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Falkowski, BJ. (1999). Risk Analysis Using Perceptrons and Quadratic Programming. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_59

Download citation

  • DOI: https://doi.org/10.1007/3-540-48774-3_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66050-7

  • Online ISBN: 978-3-540-48774-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics