Choosing v in Support Vector regression with different noise models -: Theory and experiments

被引：0

作者：

Chalimourda, A ^{[1
]}

Schölkopf, B ^{[1
]}

Smola, AJ ^{[1
]}

机构：

[1] Ruhr Univ Bochum, Inst Neuroinformat, D-44780 Bochum, Germany

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V | 2000年

关键词：

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 Support Vector (SV) regression, a parameter nu controls the number of Support Vectors and the number of points that come to lie outside of the so-called epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of nu that lead to the lowest generalization error. We find good Agreement with the values that had previously been predicted by a theoretical argument leased on the asymptotic efficiency of a simplified model of SV regression.

引用

页码：199 / 204

页数：6

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