[1]陈沅涛,徐蔚鸿,吴佳英.一种增量向量支持向量机学习算法[J].南京理工大学学报(自然科学版),2012,36(05):873.
 CHEN Yuan-tao,XU Wei-hong,WU Jia-ying.Incremental Vector Support Vector Machine Learning Algorithm[J].Journal of Nanjing University of Science and Technology,2012,36(05):873.
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一种增量向量支持向量机学习算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
36卷
期数:
2012年05期
页码:
873
栏目:
出版日期:
2012-10-31

文章信息/Info

Title:
Incremental Vector Support Vector Machine Learning Algorithm
作者:
陈沅涛12徐蔚鸿12吴佳英12
1. 南京理工大学计算机科学与技术学院,江苏南京210094; 2. 长沙理工大学计算机与通信工程学院,湖南长沙410114
Author(s):
CHEN Yuan-tao12XU Wei-hong12WU Jia-ying12
1. School of Computer Science and Technology,NUST,Nanjing 210094,China; 2. School of Computer and Communicational Engineering,Changsha University of Science and Technology, Changsha 410114,China
关键词:
支持向量机增量向量修剪约减集
Keywords:
support vector machineincrement vectorpruningsome clips
分类号:
TP391
摘要:
针对传统支持向量机方法执行效率低、耗时长的问题,该文提出一种基于增量向量支持 向量机学习(IV-SVM)方法。对训练样本集在核空间的增量向量进行训练,获得初始支持向量 机分类器。利用该分类器在Karush-Kuhn-Tucker(KKT) 条件下对初始训练样本进行修剪得到 约减集,再用该约减集对初始分类器进一步加工,得到最终的支持向量机分类器。仿真结果表 明,与传统支持向量机方法相比,在保证支持向量机泛化能力的条件下,IV-SVM 可有效降低大 容量数据样本的支持向量机训练时间。
Abstract:
In view of that the common support vector machine( SVM) learning algorithm is time- consuming and lower in efficiency,an algorithm of incremental vector support vector machine( IV- SVM) learning algorithm is put forward here. A primary SVM classifier is acquired based on the selected increment vectors in the kernel space. According to Karush-Kuhn-Tucker(KKT)conditions, the original training samples are pruned through the primary SVM classifier. The final SVM classifier is obtained by training the primary SVM classifier with reduction samples. Simulation experiments show that,compared with the common support vector machine,IV-SVM can reduce the training time of large capacity data samples support vector machine.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2011-08-02修回日期:2011-10-11 基金项目:国家自然科学青年科学基金(61001004);湖南省自然科学基金(09JJ6094);湖南省教育厅科研青年项目 (12B005);长沙市科技计划重点项目(K1104022-11);湖南省科技计划项目(2012GK3056;2011SK3079) 作者简介:陈沅涛(1980-),男,博士生,讲师,主要研究方向:模式识别与图像处理,E-mail:yufeng8552@ qq. com。
更新日期/Last Update: 2012-11-26