Parameter optimizing for Support Vector Machines classification

doi:10.3778/j.issn.1002-8331.2011.03.037

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (3): 123-124.DOI: 10.3778/j.issn.1002-8331.2011.03.037

• 数据库、信号与信息处理 • Previous Articles Next Articles

Parameter optimizing for Support Vector Machines classification

FENG Guohe

School of Economy & Manangement，South China Normal University，Guangzhou 510006，China

Received:2009-09-04 Revised:2009-11-05 Online:2011-01-21 Published:2011-01-21
Contact: FENG Guohe

SVM分类核函数及参数选择比较

奉国和

华南师范大学经济管理学院信息管理系，广州 510006

通讯作者: 奉国和

Abstract

Abstract: Support Vector Machine（SVM） has good performance for classification，but the performance is restricted by the kernel function and its parameters.This paper discusses the problem，and uses cross validation，grid searching for optimizing the kernel function parameters.

Key words: Support Vector Machines（SVM）, kernel function, classification

摘要： 支持向量机（SVM）被证实在分类领域性能良好，但其分类性能受到核函数及参数影响。讨论核函数及参数对SVM分类性能的影响，并运用交叉验证与网格搜索法进行参数优化选择，为SVM分类核函数及参数选择提供借鉴。

关键词: 支持向量机, 核函数, 分类

CLC Number:

TP316

FENG Guohe . Parameter optimizing for Support Vector Machines classification[J]. Computer Engineering and Applications, 2011, 47(3): 123-124.

奉国和. SVM分类核函数及参数选择比较[J]. 计算机工程与应用, 2011, 47(3): 123-124.

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Parameter optimizing for Support Vector Machines classification

SVM分类核函数及参数选择比较

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