Supervised learning with kernel methods

Authors:
Taouali Okba;Elaissi Ilyes;Garna Tarek;Messaoud Hassani
Affiliations:
Unité de Recherche ATSI, Ecole Nationale d'Ingénieurs de Monastir, Monastir, Tunisia;Unité de Recherche ATSI, Ecole Nationale d'Ingénieurs de Monastir, Monastir, Tunisia;Unité de Recherche ATSI, Ecole Nationale d'Ingénieurs de Monastir, Monastir, Tunisia;Unité de Recherche ATSI, Ecole Nationale d'Ingénieurs de Monastir, Monastir, Tunisia
Venue:
WAMUS'10 Proceedings of the 10th WSEAS international conference on Wavelet analysis and multirate systems
Year:
2010

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Abstract

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.