@misc{Trzęsiok_Michał_Metoda_2005,
 author={Trzęsiok, Michał},
 year={2005},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 description={Prace Naukowe Akademii Ekonomicznej we Wrocławiu. Taksonomia (12); 2005; nr 1076, s. 501-510},
 publisher={Wydawnictwo Akademii Ekonomicznej im. Oskara Langego we Wrocławiu},
 language={pol},
 abstract={For nonlinear regression problem, support vector machines (SVM) map the input space into a high-dimensional feature space first, and then perform linear regression in the high-dimensional feature space. The nonlinearity of SVM is realized by choosing the kernel function. Performance of SVM is very sensitive to the choice of the kernel and model parameters. In the paper the method is presented and the dependency of its performance on the kernel and the model parameters selection is analyzed.},
 type={artykuł},
 title={Metoda wektorów nośnych w konstrukcji nieparametrycznych modeli regresji},
}