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A modulated Parzen-windows approach for probability density estimation

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Advances in Intelligent Data Analysis Reasoning about Data (IDA 1997)

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

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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.

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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
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  2. J. C. A. van der Lubbe
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  3. E. Backer
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Editor information

Xiaohui Liu Paul Cohen Michael Berthold

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© 1997 Springer-Verlag

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63346-4

  • Online ISBN: 978-3-540-69520-2

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