Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

被引：234

作者：

Keerthi, SS ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Mech Engn, Singapore 119260, Singapore

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 05期

关键词：

hyperparameter tuning; support vector machines (SVMS);

D O I：

10.1109/TNN.2002.1031955

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L-2 soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.

引用

页码：1225 / 1229

页数：5

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