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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 232615.0, MPI für biologische Kybernetik / Biologische Kybernetik
Implicit estimation of Wiener series
Authors:Franz, M.O.; Schölkopf, B.
Editors:Barros, A.; Principe, J.; Larsen, J.; Adali, T.; Douglas, S.
Date of Publication (YYYY-MM-DD):2004
Title of Proceedings:Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop
Start Page:735
End Page:744
Physical Description:10
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2643
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