..
Suche
Hinweise zum Einsatz der Google Suche
Personensuchezur unisono Personensuche
Veranstaltungssuchezur unisono Veranstaltungssuche
Katalog plus

6. Classification

Least Squares Support Vector Machine

Properties

  • Robustly solves high-dimensional problems.
  • Least squares support vector machines with RBF kernels → computational efficiency.
  • Two hyper-parameters: Standard deviation of Gaussian RBFs & regularization strength.
  • Hyper-parameters can be adjusted according minimal leave-one-out error.
  • Leave-one-out error can be computed free-of-charge in LS-SVMs: N x speedup.
  • 2- and 1-class versions.

Applications

  • Design space exploration in combustion engine measurement.
  • Description of non-convex areas in combustion engine measurement
  • Modeling the stall limit in computational fluid dynamics (CFD) simulations.

Design Space Exploration

f69_b01

 

Next Chapter: 7. Design of Excitation Signals for Identification     Back to Overview