Kernel Ho-Kashyap classifier with generalization control

• Autorzy: Jacek Łęski
• 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

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

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: 14
• Numer: 1
• Strony: 53-61

Daty

wydano

2004

otrzymano

2003-08-18

poprawiono

2004-01-05

Twórcy

autor

Jacek Łęski

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

Bibliografia

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