Kernel Ho-Kashyap classifier with generalization control

• Autorzy: Łęski J.
• Języki publikacji: EN

Abstrakty

EN

This paper introduces a new classifier design method based on a kernel extension of the classical Ho-Kashyap procedure. The proposed method uses an approximation of the absolute error rather than the squared error to design a classifier, which leads to robustness against outliers and a better approximation of the misclassification error. Additionally, easy control of the generalization ability is obtained using the structural risk minimization induction principle from statistical learning theory. Finally, examples are given to demonstrate the validity of the introduced method.

Słowa kluczowe

EN

kernel methods; classifier design; Ho-Kashyap classifier; generalization control; robust methods

PL

metoda jądrowa; metoda odporna; projekt klasyfikatora

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: Vol. 14, no 1
• Strony: 53--61
• Opis fizyczny: Bibliogr. 26 poz., tab., wykr.

Twórcy

autor

Łęski J.

• Institute of Electronics Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland,

Bibliografia

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