[1]周欣,许建华.基于遗传算法与经验误差最小化的SVM模型选择方法[J].南京师范大学学报(工程技术版),2009,09(02):065-71.
 Zhou Xin,Xu Jianhua.SVM Model Selection Based on Genetic Algorithms and Empirical Error Minimization[J].Journal of Nanjing Normal University(Engineering and Technology),2009,09(02):065-71.
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基于遗传算法与经验误差最小化的SVM模型选择方法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
09卷
期数:
2009年02期
页码:
065-71
栏目:
出版日期:
2009-06-30

文章信息/Info

Title:
SVM Model Selection Based on Genetic Algorithms and Empirical Error Minimization
作者:
周欣;许建华;
南京师范大学计算机科学与技术学院, 江苏南京210097
Author(s):
Zhou XinXu Jianhua
School of Computer Sciences,Nanjing Normal University,Nanjing 210097,China
关键词:
支持向量机 核函数 核参数 经验误差 遗传算法
Keywords:
support vector m ach ine ke rnel func tion kerne l param e ter em pir ica l erro r genetic a lgor ithm
分类号:
TP18
摘要:
支持向量机(SVM)的推广能力依赖于核函数形式及核参数和惩罚因子的选取,即模型选择.在分析参数对分类器识别精度的影响基础上,提出了基于遗传算法和经验误差最小化的支持向量机参数选择方法.在13个UC I数据集上的实验表明了本文算法的正确性与有效性,且具有良好的推广性能.
Abstract:
The spread ing capacity o f support vec to rm ach ine ( SVM ) depends large ly on the se lec tion o f kerne l function and its param eters, and penalty facto r, tha t ism ode l selection. H av ing ana lyzed the param ete rs’ in fluence on the classif-i e rs’ recogn ition accuracy, w e propose a new m ethod for SVM model se lection using genetic algor ithm and em pir ica l e rror m inim ization. The exper im ents on 13 diffe rent UC I benchm a rks show its correc tness, effec tiveness and good spreading perform ance.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60875001)资助项目.
通讯联系人: 许建华, 教授, 研究方向: 模式识别、机器学习、信号处理等. E-m ail: xujianhua@ njnu. edu. cn
更新日期/Last Update: 2013-04-23