Automatic hyperparameter tuning for support vector machines

被引：0

作者：

Anguita, D ^{[1
]}

Ridella, S ^{[1
]}

Rivieccio, F ^{[1
]}

Zunino, R ^{[1
]}

机构：

[1] Univ Genoa, Dept Biophys & Elect Engn, DIBE, I-16145 Genoa, Italy

来源：

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

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：

This work describes the application of the Maximal Discrepancy (MD) criterion to the process of hyperpaxameter setting in SVMs and points out the advantages of such an approach over existing theoretical and practical frameworks. The resulting theoretical predictions axe compared with a k-fold cross-validation empirical method on some benchmark datasets showing that the MD technique can be used for automatic SVM model selection.

引用

页码：1345 / 1350

页数：6

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