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6. Classification

Least Squares Support Vector Machine


  • 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.


  • 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



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