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