[1]刘成忠.两种不确定支持向量机分类性能的对比研究[J].计算机技术与发展,2011,(11):156-159.
　LIU Cheng-zhong.Comparative Study on Classification Performances of Two Indeterminate Support Vector Machines[J].,2011,(11):156-159.
点击复制

两种不确定支持向量机分类性能的对比研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年11期
页码:
156-159
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Comparative Study on Classification Performances of Two Indeterminate Support Vector Machines
文章编号:
1673-629X（2011）11-0156-04
作者:
刘成忠
甘肃农业大学信息科学技术学院
Author(s):
LIU Cheng-zhong
College of Information Science ＆ Technology,Gansu Agricultural University
关键词:
支持向量机; 模糊理论; 粗糙集
Keywords:
support vector machine; fuzzy theory;  rough set
分类号:
TP31
文献标志码:
A
摘要:
为了克服支持向量机方法对于噪声或孤立野值点敏感的问题，通过引入模糊理论与粗糙集方法，可以分另9得到两种不确定支持向量机模型。文中通过分析和比较模糊支持向量机和粗糙支持向量机分类模型构造方法，解释了这两种不确定支持向量机模型克服噪声影响的原理。同时通过一个合成数据集和一组标准数据集对这两种不确定支持向量机的泛化性能进行了对比验证。实验结果表明，相比传统支持向量机，两种不确定支持向量机都能不同程度地提高分类精度，并且模糊支持向量机算法整体表现出了更好的泛化性能
Abstract:
In order to overcome the problem that support vector machine is ,sensitive to the noise and isolated outliers, introduce fuzzy theory and rough set theory into support vector machine to get two kinds of indeterminate support vector machines. Through analysis and comparison of the construction method of fuzzy support vector raachine and that of rough support vector machine, the principles of the two indeterminate methods reducing the outliers are explained. At the same time, generalization performances of the two indeterminate support vector machines are comparatively verified through a synthetic data set and a set of standard data. Experiment remits show that the two indeterminate methods have better performances of reducing oufliers than traditional support vector machine, that they can significantly improve the classification accuracy, and that fuzzy support vector machine has a better generalization performance oh the whole

相似文献/References:

[1]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
　LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(11):125.
[2]陈俏 曹根牛 陈柳.支持向量机应用于大气污染物浓度预测[J].计算机技术与发展,2010,(01):247.
　CHEN Qiao,CAO Gen-niu,CHEN Liu.Application of Support Vector Machine to Atmospheric Pollution Prediction[J].,2010,(11):247.
[3]李晶 姚明海.基于支持向量机的语义图像分类研究[J].计算机技术与发展,2010,(02):75.
　LI Jing,YAO Ming-hai.Research of Semantic Image Classification Based on Support Vector Machine[J].,2010,(11):75.
[4]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
　JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(11):17.
[5]曹庆璞 董淑福 罗赟骞.网络时延的混沌特性分析及预测[J].计算机技术与发展,2010,(04):43.
　CAO Qing-pu,DONG Shu-fu,LUO Yun-qian.Chaotic Analysis and Prediction of Internet Time- Delay[J].,2010,(11):43.
[6]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(11):84.
[7]黄炜 黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,(06):21.
　HUANG Wei,HUANG Zhi-hua.Feature Selection Based on Genetic Algorithm and SVM[J].,2010,(11):21.
[8]孙秋凤.microRNA计算识别中的模式识别技术[J].计算机技术与发展,2010,(06):97.
　SUN Qiu-feng.Pattern Recognition Technology for MicroRNA Identification[J].,2010,(11):97.
[9]刘振岩 王勇 陈立平 马俊杰 陈天恩.基于SVM的农业智能决策Web服务的研究与实现[J].计算机技术与发展,2010,(06):213.
　LIU Zhen-yan,WANG Yong,CHEN Li-ping,et al.Research and Implementation of Intelligence Decision Web Services Based on SVM for Digital Agriculture[J].,2010,(11):213.
[10]王李冬.一种新的人脸识别算法[J].计算机技术与发展,2009,(05):147.
　WANG Li-dong.A New Algorithm of Face Recognition[J].,2009,(11):147.

备注/Memo

备注/Memo:
甘肃省自然科学基金（096PdZA004）;甘肃省教育科研基金（0902-04）;甘肃省科技支撑计划（1011NKCA058）刘成忠（1969-），男，甘肃天祝人，副教授，研究方向为智能决策支持系统
更新日期/Last Update: 1900-01-01