TL-SVM:一种迁移学习算法  

TL-SVM: A transfer learning algorithm

作　　者：许敏[1,2] 王士同[1] 顾鑫[1] 

机构地区：[1]江南大学数字媒体学院,江苏无锡214122 [2]无锡职业技术学院物联网技术学院,江苏无锡214121

出　　处：《控制与决策》2014年第1期141-146,共6页Control and Decision

基　　金：国家自然科学基金项目(61272210;61170122);江苏省研究生创新工程项目(CXZZ12-0759)

摘　　要：迁移学习旨在利用大量已标签源域数据解决相关但不相同的目标域问题.当与某领域相关的新领域出现时,若重新标注新领域,则样本代价昂贵,丢弃所有旧领域数据又十分浪费.对此,基于SVM算法提出一种新颖的迁移学习算法—–TL-SVM,通过使用目标域少量已标签数据和大量相关领域的旧数据来为目标域构建一个高质量的分类模型,该方法既继承了基于经验风险最小化最大间隔SVM的优点,又弥补了传统SVM不能进行知识迁移的缺陷.实验结果验证了该算法的有效性.Transfer learning（TL） aims to solve related but different target domain problems by using plenty of labeled source domain data. When the task from one new domain comes, new domain samples are relabeled costly, and it would be a waste to discard all the old domain data. Therefore, an algorithm called TL-SVM based on the SVM algorithm is proposed, which uses a small amount of target domain tag data and a large number of source domain old data to build a high-quality classification model. The method inherits the advantages of the maximum interval SVM based on empirical risk minimization and makes up for the defects that traditional SVM can not migrate knowledge. Experimental results show the effectiveness of the proposed algorithm.

关 键 词：迁移学习 分类 支持向量机 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]