SVM增量学习中的概念迁移问题及处理方法  

Concept drift on SVM incremental learning and its disposals

作　　者：白冬婴[1] 王晓丹[1] 张宏达[1] 权文[1] 

机构地区：[1]空军工程大学导弹学院计算机工程系,陕西三原713800

出　　处：《计算机工程与设计》2008年第10期2619-2621,共3页Computer Engineering and Design

基　　金：国家自然科学基金项目(50505051);陕西省自然科学研究计划基金项目(2007F19);空军工程大学导弹学院研究生学位论文创新基金项目(DY06205)

摘　　要：支持向量机由于其自身的特点使其在许多应用中表现出了特有的优势,是目前研究的热点。由于标准的SVM学习算法并不直接支持增量式学习,所以研究有效的SVM增量学习方法具有重要理论意义和实用价值。对SVM增量学习中动态目标学习的有关问题进行了深入讨论,定义了静态目标学习与动态目标学习。针对动态目标学习提出了概念迁移问题,给出了SVM增量学习概念迁移的数学表达。讨论和分析了现有的SVM增量学习方法、以及目前处理SVM增量学习中概念迁移问题的方法并得出了结论。Support vector machine reveals its own advantages in many applications for its inherent characteristics and becomes an attractive research area these years. The standard algorithm of support vector machine cannot support incremental learning, therefore, researches on the method of effective incremental learning are of theoretical and practical important. The problem of learning on moving target in incremental learning is discussed. After giving the definition of static target learning and moving target learning, the problem of concept drift for learning on moving target is proposed, and the expression of concept drift for SVM-based incremental learning is given. The approaches of SVM-based incremental learning are discussed and analyzed, and the conclusions are given after analyzing the current approaches of disposing the concept drift on SVM-based incremental learning.

关 键 词：支持向量机 增量学习 支持向量 动态目标 概念迁移 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]