Robust cross-validation score function for non-linear function estimation

被引：0

作者：

De Brabanter, J ^{[1
]}

Pelckmans, K ^{[1
]}

Suykens, JAK ^{[1
]}

Vandewalle, J ^{[1
]}

机构：

[1] Katholieke Univ Leuven, ESAT, SCD, SISTA, B-3001 Heverlee, Belgium

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2002 | 2002年 / 2415卷

关键词：

weighted LS-SVM; robust cross-validation score function; influence functions; breakdown point; M-estimators and L-estimators;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a new method for tuning regularisation parameters or other hyperparameters of a learning process (non-linear function estimation) is proposed, called robust cross-validation score function (CVS-foldRobust). CVS-foldRobust is effective for dealing with outliers and non-Gaussian noise distributions on the data. Illustrative simulation results are given to demonstrate that the CVS-foldRobust method outperforms other cross-validation methods.

引用

页码：713 / 719

页数：7

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