摘要
基于密度差(Difference Of Density,DOD)思想,L2核分类器算法具有良好的分类性能及稀疏性,然而其训练域与测试域独立同分布的假设限制了其应用范围。针对此不足,该文提出一种新的面向迁移学习的L2核分类器(Transfer Learning-L2 Kernel Classification,TL-L2KC),该方法既保持了L2核分类器算法良好的分类性能,又能处理数据集缓慢变化及训练集在特定约束条件下获得导致训练集和未来测试集分布不一致的问题。基于人造数据集和UCI真实数据集的实验表明,该文提出的TL-L2KC算法较之于经典的迁移学习分类方法,具有相当的、甚至更好的性能。
Based on the concept of Difference Of Density （DOD）, L2 Kernel Classifier（L2KC） exhibits its good performance. However, the assumption that the training domain and testing domain are independent and identically distributed severely constrains its usefulness. In order to overcome this shortcoming, a novel classifier named Transfer Learnging-L2 Kernel Classification （TL-L2KC） is proposed for the changing environment. The proposed classifier can not only inherit the advantage of L2KC, but also deal with the problem that the distribution inconsistency between the training and testing sets which is caused by the slow change of the datasets or the training set obtained with specific constraints. As demonstrated by extensive experiments in simulation datasets and UCI benchmark datasets, the proposed classifier TL-L2KC shows the performance which is comparable to or better than that of the classical algorithms on the transfer learning classification problems.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第9期2059-2065,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61272210
61170122)
江苏省研究生创新工程项目(CXZZ12-0759)资助课题
关键词
支持向量机
迁移学习
密度差
L2核分类器
Support Vector Machine （SVM）
Transfer learning
Difference Of Density （DOD）
L2 KernelClassification （L2KC）
