@inproceedings{2643,
  title = {Implicit estimation of Wiener series},
  journal = {Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop},
  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.},
  pages = {735-744},
  editors = {A Barros  and J Principe and J Larsen and T Adali and S Douglas},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York},
  year = {2004},
  author = {Franz, MO. and Sch{\"o}lkopf, B.}
}