Maximal variation and missing values for componentwise support vector machines

被引：0

作者：

Pelckmans, K ^{[1
]}

Suykens, JAK ^{[1
]}

De Moor, B ^{[1
]}

机构：

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

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5 | 2005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：

This paper proposes primal-dual kernel machine classifiers based on worst-case analysis of a finite set of observations including missing values of the inputs. Key ingredients are the use of a componentwise Support Vector Machine (cSVM) and an empirical measure of maximal variation of the components to bound the influence of the component which can not be evaluated due to missing values. A regularization term based on the L, norm of the maximal variation is used to obtain a mechanism for structure detection in that context. An efficient implemtation using the hierarchical kernel machines framework is elaborated.

引用

页码：2814 / 2819

页数：6

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