[1]李雷 房小萍 张宁.一种基于几何分布的新支持向量机多分类方法[J].计算机技术与发展,2012,(11):172-175.
　LI Lei,FANG Xiao-ping,ZHANG Ning.A New SVM Multi-classification Method Based on Geometric Distribution[J].,2012,(11):172-175.
点击复制

一种基于几何分布的新支持向量机多分类方法()
分享到：

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

卷:
期数:
2012年11期
页码:
172-175
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A New SVM Multi-classification Method Based on Geometric Distribution
文章编号:
1673-629X（2012）11-0172-04
作者:
李雷1 房小萍1 张宁2
[1]南京邮电大学理学院[2]南京邮电大学自动化学院
Author(s):
LI Lei ;  FANG Xiao-ping ;  ZHANG Ning
[1]College of Science, Nanjing University of Posts and Telecommunications[2]College of Automation, Nanjing University of Posts and Telecommunications
关键词:
二叉树; 支持向量机; 多分类
Keywords:
binary tree ;  SVM;  multi-classification
分类号:
TP31
文献标志码:
A
摘要:
:二叉树支持向量机是多分类问题的一种有效方法，然而分类的效果与二叉树的结构密切相关。获得更好的分类效果和更高的效率，要使得二叉树高度尽量小而两个子类尽量易分。距离通常用来衡量两个类的分离程度，但不能反映类的分布情况。考虑到多分类中类的分布，文中定义新的分离度和相似度来衡量两个类的分离度，并且提出了一中新的基于几何分布二叉树支持向量机多分类算法，该方法使得二叉树高度尽量小而两个子类尽量易分。实验表明该方法具有较高的分类准确率和效率
Abstract:
BTSVM is an effective approach for solving multi-class problems. However, the classification performance of the classifier is closely related to the tree structure. In order to get better performance and higher efficiency,it is necessary to make the two sub-classes more separable and make binary tree less hierarehicals. Distance measure is common used as a separability measure between classes,but it does not reflect the distribution of the classes. In consideration of distribution, a new separability measure and a new similarity measure are defined to measure the separability and similarity between classes. Moreover,a novel geometricdistribution-based BTSVM is proposed to make the two sub-classes more separable and binary tree less hierarchicals. Experiments made on three data sets prove the high effi- ciency and good performance of the proposed algorithm

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]杜薇薇 张翼燕 瞿春柳.基于平衡因子的AVL树设计实现[J].计算机技术与发展,2010,(03):24.
　DU Wei-wei,ZHANG Yi-yan,QU Chun-liu.AVL Tree Design and Implementation Based on Balancing Factor[J].,2010,(11):24.
[6]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.
[7]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.
[8]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.
[9]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.
[10]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.
[11]朱程辉 项思俊.手写体汉字识别的二叉树SVM算法研究[J].计算机技术与发展,2009,(09):42.
　ZHU Cheng-hui,XIANG Si-jun.Study of Handwritten Chinese Characters Recognition Based on Binary Tree SVM Algorithm[J].,2009,(11):42.
[12]吕蕾 刘弘.基于支持向量机的小区规划方案评价方法[J].计算机技术与发展,2009,(01):193.
　LV Lei,LIU Hong.Study on Evaluation Method of Programme Scheme of Residential Quarter Based on SVM[J].,2009,(11):193.
[13]徐勇 张海 周森鑫 王辉.基于统计学习理论的人脸识别方法研究[J].计算机技术与发展,2007,(11):118.
　XU Yong,ZHANG Hai,ZHOU Sen-xin,et al.Research on Face Recognition Based on Statistical Learning Theory[J].,2007,(11):118.
[14]王辉.主成分分析及支持向量机在人脸识别中的应用[J].计算机技术与发展,2006,(08):24.
　WANG Hui.Application in Human Face Recognition Based on Principal Component Analysis and Support Vector Machine[J].,2006,(11):24.
[15]李燕玲,苏一丹. 改进的二叉树支持向量机在多分类中的应用[J].计算机技术与发展,2014,24(07):181.
　LI Yan-ling,SU Yi-dan. Application of Improved Binary Tree Support Vector Machine in Multi-classification[J].,2014,24(11):181.
[16]李琼,陈利. 一种改进的支持向量机文本分类方法[J].计算机技术与发展,2015,25(05):78.
　LI Qiong CHEN Li. An Improved Text Classification Method for Support Vector Machine[J].,2015,25(11):78.

备注/Memo

备注/Memo:
国家自然科学基金资助项目（61070234,61071167）;江苏省高校自然科学基金（08KTB520003）李雷（1958-），男，安徽砀山人，教授，研究方向为模糊数学和智能系统、信号处理、非线性分析；房小萍（1988-），女，江苏南通人，硕士研究生，从事模糊数学和智能系统的研究
更新日期/Last Update: 1900-01-01