摘要
针对与测试数据分布相同的训练数据不足,相关领域中存在大量的、与测试数据分布相近的训练数据的场景,提出一种基于相似度学习的多源迁移学习算法(SL-MSTL).该算法在经典SVM分类模型的基础上提出一种新的迁移分类模型,增加对多源域与目标域之间的相似度学习,可以有效地利用各源域中的有用信息,提高目标域的分类效果.实验的结果表明了SL-MSTL算法的有效性和实用性.
For the proplem that the training data which have the same distribution with the test data are insuficient, but a lot of training data which have the similar distribution with the test data exist in the related field, a similarity-learning based multi-source transfer learning（SL-MSTL） algorithm is proposed. A similarity-learning based classification model is proposed in contrast to the classical support vector machine（SVM） model. Compared to the SVM model, the proposed similarity-learning based model can make better use of the source information and improve the classification performance.Experimental results show the effectiveness and the practicality of the proposed algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第11期1941-1948,共8页
Control and Decision
基金
国家自然科学基金项目(61170122
61272210)
江苏省自然科学基金项目(BK20130155)
关键词
相似度学习
多源域
迁移学习
SVM
迁移分类
similarity learning
multi-source
transfer learrdng
SVM
transfer classification
