Kernel Ho-Kashyap classifier with generalization control
International Journal of Applied Mathematics and Computer Science (2004)
- Volume: 14, Issue: 1, page 53-61
- ISSN: 1641-876X
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topŁęski, Jacek. "Kernel Ho-Kashyap classifier with generalization control." International Journal of Applied Mathematics and Computer Science 14.1 (2004): 53-61. <http://eudml.org/doc/207678>.
@article{Łęski2004,
abstract = {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.},
author = {Łęski, Jacek},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {classifier design; Ho-Kashyap classifier; kernel methods; robust methods; generalization control},
language = {eng},
number = {1},
pages = {53-61},
title = {Kernel Ho-Kashyap classifier with generalization control},
url = {http://eudml.org/doc/207678},
volume = {14},
year = {2004},
}
TY - JOUR
AU - Łęski, Jacek
TI - Kernel Ho-Kashyap classifier with generalization control
JO - International Journal of Applied Mathematics and Computer Science
PY - 2004
VL - 14
IS - 1
SP - 53
EP - 61
AB - 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.
LA - eng
KW - classifier design; Ho-Kashyap classifier; kernel methods; robust methods; generalization control
UR - http://eudml.org/doc/207678
ER -
References
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