Incremental Training for SVM-Based Classification with Keyword Adjusting

作     者：SUNJin-wen YANGJian-wu LUBin XIAOJian-guo 

作者机构：NationalKeyLaboratoryforTextProcessing.InstituteofComputerScienceandTechnology.PekingUniversity.Beijing100871.China 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2004年第9卷第5期

页      面：805-811页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：theNationalInformationIndustryDevelopmentFoundationofChina 

主　　题：SVM (support vector machine) incremental training classification keyword adjusting 

摘      要：This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning. According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting. The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method. Key words SVM (support vector machine) - incremental training - classification - keyword adjusting CLC number TP 18 Foundation item: Supported by the National Information Industry Development Foundation of China Biography: SUN Jin-wen (1972-), male, Post-Doctoral, research direction: artificial intelligence, data mining and system integration.