A novel online learning algorithm of support vector machines

被引：0
|
作者：
Mu, Shaomin [1 ,2 ]
Tian, Shengfeng [1 ]
Yin, Chuanhuan [1 ]
机构：
[1] Beijing Jiao Tong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
[2] Shandong Agr Univ, Sch Comp & Informat Technol, Tai An 271018, Shandong, Peoples R China
来源：
2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4 | 2006年
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Support vector machines (SVMs) have been proven as powerful tools in wide variety of learning problems, but it is confronted with the problem of a large amount of computation. In this paper, the existing online learning algorithms of SVMs have been discussed in detail, we analysis the possible changes of support vectors after new samples are added, a novel online learning algorithm of SVMs is presented. The experimental results are given to show that the accuracies of approach is comparable to the batch algorithm, effectively keep classification accuracies, discard useless old samples, and save the memory.
引用
收藏
页码：1927 / +
页数：2
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