Multi-kernel growing Support Vector Regressor

被引：0

作者：

Gutiérrez-González, D ^{[1
]}

Parrado-Hernández, E ^{[1
]}

Navia-Vázquez, A ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Dept Signal Proc & Commun, Madrid 28911, Spain

来源：

COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS | 2005年 / 3512卷

关键词：

CLASSIFIERS;

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 presents a method to iteratively grow a compact Support Vector Regressor so that the balance between size of the machine and its performance can be user-controlled. The algorithm is able to combine Gaussian kernels with different spread parameter, skipping the 'a priori' parameter estimation by allowing a progressive incorporation of nodes with decreasing values of the spread parameter, until a cross-validation stopping criterion is met. Experimental results show the significant reduction achieved in the size of the machines trained with this new algorithm and their good generalization capabilities.

引用

页码：357 / 365

页数：9

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