Skip to main content
Springer Nature Link
Log in

A modulated Parzen-windows approach for probability density estimation

  • Conference paper
  • First Online:
Advances in Intelligent Data Analysis Reasoning about Data (IDA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1280))

Included in the following conference series:

  • 756 Accesses

Abstract

The Parzen-window approach is a well-known technique for estimating probability density functions. This paper introduces a modulated Parzen-windows approach. This approach uses kernels at equidistant samples to obtain a probability density function more efficiently. Experiments on both artificial and real data show that the modulated Parzen-windows approach is more efficient in probability density function estimation, without costly preprocessing or severe loss of accuracy.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G.A. Babich and O.I. Campus, Weighted Parzen Windows for Pattern Classification, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 567–570, 1996.

    Article  Google Scholar 

  2. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons Inc., 1973.

    MATH  Google Scholar 

  3. J. Fan and J. S. Marron, Fast Implementations of Nonparametric Curve Estimators, J. Computational and Graphical Statistics, vol 3, pp. 35–56, 1994

    Google Scholar 

  4. R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, vol. 7, pp. 179–188, 1936

    Article  Google Scholar 

  5. K. Pukunaga, Statistical Pattern Recognition, San Diego, Calif: Academic Press Inc., 1990

    Google Scholar 

  6. L.B. Gamage, R.G. Gosine and C.W. de Silva, Extraction of Rules from Natural Objects for Automated Mechanical Processing, IEEE Trans. Syst., Man, Cybern., vol. 26, pp. 105–120, 1996.

    Google Scholar 

  7. R.J. Marks II, Introduction to Shannon Sampling and Interpolation Theory, New York: Springer-Verlag Inc., 1991

    Book  MATH  Google Scholar 

  8. E. Parzen, On estimation of a probability density function and mode, Ann. Math. Statistics, vol. 33, pp. 1065–1076, 1962.

    Article  MATH  MathSciNet  Google Scholar 

  9. S.J. Raudys and A.K. Jain, Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners, IEEE Trans. Pattern Analysis and Machine Intelligence vol. 13, pp. 252–264, 1991.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Delft University of Technology, Mekelweg 4, 2628, CD Delft, The Netherlands

    G. C. van den Eijkel, J. C. A. van der Lubbe & E. Backer

Authors
  1. G. C. van den Eijkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. C. A. van der Lubbe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Backer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Xiaohui Liu Paul Cohen Michael Berthold

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag

About this paper

Cite this paper

van den Eijkel, G.C., van der Lubbe, J.C.A., Backer, E. (1997). A modulated Parzen-windows approach for probability density estimation. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052864

Download citation

  • DOI: https://doi.org/10.1007/BFb0052864

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63346-4

  • Online ISBN: 978-3-540-69520-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics