Hierarchical learning in polynomial support vector machines

被引：0

作者：

Risau-Gusman, S ^{[1
]}

Gordon, MB ^{[1
]}

机构：

[1] CEA Grenoble, DRFMC, SPSMS, F-38054 Grenoble 09, France

来源：

MACHINE LEARNING | 2002年 / 46卷 / 1-3期

关键词：

learning theory; support vector machines;

D O I：

10.1023/A:1012442008732

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that takes into account the number of high order features relative to the input space dimension. We analyze the effect of different features' normalizations on the generalization error, for different kinds of learning tasks. If the normalization is adequately selected, hierarchical learning of features of increasing order takes place as a function of the training set size. Otherwise, the performance worsens, and there is no hierarchical learning at all.

引用

页码：53 / 70

页数：18

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