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  Implicit estimation of Wiener series

Franz, M., & Schölkopf, B. (2004). Implicit estimation of Wiener series. In A. Barros, J. Principe, J. Larsen, T. Adali, & S. Douglas (Eds.), 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing 2004 (pp. 735-744). Piscataway, NJ, USA: IEEE Operations Center.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F397-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-8DB7-C
Genre: Conference Paper

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MLSP-2004-Franz.pdf (Any fulltext), 192KB
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 Creators:
Franz, MO1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hilbert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

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 Dates: 2005-042004-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2643
 Degree: -

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Title: 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing (MLSP 2004)
Place of Event: Sao Luis, Brazil
Start-/End Date: 2004-09-29 - 2004-10-01

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Title: 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing 2004
Source Genre: Proceedings
 Creator(s):
Barros, A, Editor
Principe, J, Editor
Larsen, J, Editor
Adali, T, Editor
Douglas, S, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE Operations Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 735 - 744 Identifier: ISBN: 0-7803-8608-4