Supervised learning with kernel methods
Okba Taouali, Ilyes Elaissi, Tarek Garna, Hassani Messaoud · 2010
This paper proposes a comparative study of three identification kernel methods of nonlinear systems modelled in Reproducing Kernel Hilbert Space (RKHS), where the model output results from a linear combination of kernel functions. The coefficients of this combination are the model parameters, the number of which equals the number of observations used in learning phase. Theses methods are support vector machines (SVM), regularization networks (RN) and kernel Principal Component Analysis (KPCA). The performances of each method in terms of generalization ability and computing time were evaluated on numerical simulations.