[1]薛松,李雷. 基于类内离散度的最小二乘支持向量机[J].计算机技术与发展,2015,25(04):71-74.
　XUE Song,LI Lei. Least Squares Support Vector Machine Based on Within-class Scatter[J].,2015,25(04):71-74.
点击复制

 基于类内离散度的最小二乘支持向量机()
分享到：

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

卷:
25
期数:
2015年04期
页码:
71-74
栏目:
智能、算法、系统工程
出版日期:
2015-04-10

文章信息/Info

Title:
 Least Squares Support Vector Machine Based on Within-class Scatter
文章编号:
1673-629X（2015)04-0071-04
作者:
 薛松; 李雷
 南京邮电大学 理学院
Author(s):
 XUE Song; LI Lei
关键词:
 ; 类内离散度; 分类; 最小二乘支持向量机; 核方法
Keywords:
 ; within-class scatter; classification; least squares support vector machine; kernel methods
分类号:
TP31
文献标志码:
A
摘要:
 支持向量机是一种很流行的机器学习方法，在许多领域都有了广泛的使用。传统的支持向量机模型是寻求类之间的间隔最大化，而忽视了一个重要的信息—样本的类内结构，类内离散度。文中将Fisher判别分析里面的类内离散度引入到最小二乘支持向量机中，提出了基于类内离散度的最小二乘支持向量机模型。并通过核函数将样本映射到高维特征空间，在特征空间中进行样本分类。基于UCI数据库的数据集实验测试表明，基于类内离散度的最小二乘支持向量机提高了分类的准确度。
Abstract:
 Support vector machine ( SVM) is a popular machine learning technique,and it has been widely used in many real-world ap-plications. Traditional SVMs aims at seeking the hyperplane that maximizes the margin and ignores an important prior knowledge, the within-class structure. It formulates a Least Square Support Vector Machine ( LSSVM) based on within-class scatter for binary classifi-cation,which incorporates minimum within-class scatter in Fisher Discriminant Analysis ( FDA) into LSSVM. The sample points are mapped to a high dimensional feature space where the samples are classified by using the kernel method. Experiments on four benchmark-ing datasets based on UCI show that the proposed WCS-LSSVM can improve the classification accuracy.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(04):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(04):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(04):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(04):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(04):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(04):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(04):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(04):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(04):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(04):47.

更新日期/Last Update: 2015-06-04