[1]王健峰,张磊,陈国兴,等.基于改进的网格搜索法的SVM参数优化[J].应用科技,2012,39(03):28-31.[doi:10.3969/j.issn.1009-671X.201112016]
 WANG Jianfeng,ZHANG Lei,CHEN Guoxing,et al.A parameter optimization method for an SVM based on improved grid search algorithm[J].yykj,2012,39(03):28-31.[doi:10.3969/j.issn.1009-671X.201112016]
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基于改进的网格搜索法的SVM参数优化(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第39卷
期数:
2012年03期
页码:
28-31
栏目:
现代电子技术
出版日期:
2012-06-05

文章信息/Info

Title:
A parameter optimization method for an SVM based on improved grid search algorithm
文章编号:
1009-671X(2012)03-0028-04
作者:
王健峰张磊陈国兴何学文
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
WANG Jianfeng ZHANG Lei CHEN Guoxing HE Xuewen
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
支持向量机参数优化网格搜索遗传算法粒子群算法说话人识别
Keywords:
support vector machines parameters optimization grid search genetic algorithm particle swarm optimization speaker recognition
分类号:
TP273
DOI:
10.3969/j.issn.1009-671X.201112016
文献标志码:
A
摘要:
比较了现今应用比较广泛的3种支持向量机(SVM)参数优化方法. 具体分析了网格法、遗传算法和粒子群算法在SVM参数优化方面的性能以及优缺点,提出了一种改进的网格法. 先在较大范围内进行搜索,在得到的优化结果附近区域再进行精确搜索. 实验表明改进的网格搜索法耗时短,更适用于有时间要求的说话人识别应用中.
Abstract:
Three kinds of SVM parameters optimization methods were compared in this paper. The performances and characteristics of grid search, genetic algorithm and particle swarm optimization in SVM parameters optimization were analyzed. In this paper an improved grid search method was proposed. Firstly, we searched a set of parameters in a large space and then searched accurately around the parameters we had found. The simulation shows that the improved method ueses less time and is suitable for the applications of speaker recognition limited by time.

参考文献/References:

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更新日期/Last Update: 2012-08-30