An incremental learning algorithm for support vector machine

Author

An, Jin-long ; Wang, Zhengou ; Ma, Zhen-ping

Author_Institution

Inst. of Syst. Eng., Tianjin Univ., China

Volume

2

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

1153

Abstract

The traditional SVM does not support incremental learning. And the traditional training method of SVM is not working 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.

Keywords

learning (artificial intelligence); support vector machines; SVM; classification incremental learning algorithm; iteration algorithm; storage cost; support vector machine; traditional training method; Equations; History; Machine learning; Machine learning algorithms; Quadratic programming; Read-write memory; Risk management; Support vector machine classification; Support vector machines; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259659

Filename

1259659