A Data Complexity Approach to Kernel Selection for Support Vector Machines

被引：0

作者：

Valerio, Roberto ^{[1
]}

Vilalta, Ricardo ^{[1
]}

机构：

[1] Univ Houston, Dept Comp Sci, 4800 Calhoun Rd, Houston, TX 77204 USA

来源：

PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2014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：

We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our results show how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart in polynomial kernels. By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-generalization. Our goal is to predict which data sets are more favorable to particular kernels (Gaussian or polynomial). The end result is a framework to improve the model selection process in Support Vector Machines.

引用

页码：3138 / 3139

页数：2

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