An incremental learning algorithm for support vector machine

被引：10

作者：

An, YL ^{[1
]}

Wang, ZO ^{[1
]}

Ma, ZP ^{[1
]}

机构：

[1] Tianjin Univ, Inst Syst Engn, Tianjin 300072, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

support vector machine (SVM); classification; incremental learning; iiteration algorithm;

D O I：

10.1109/ICMLC.2003.1259659

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The traditional SVM does not support incremental learning. And the traditional training method of SVM will not work when the amount of training samples are so large that they can not be put into the RAM of computer. In order to solve this problem and improve the speed of training SVM, the natural characteristics of SV are analyzed in this paper. An incremental learning algorithm (I-SVM) for SVM with discarding part of history samples is presented. The theoretical analysis and experimental results show that this algorithm can not only speed up the training process, but also reduce the storage cost, while the classification precision is also guaranteed.

引用

页码：1153 / 1156

页数：4