自动化学报2013，Vol.39Issue(8)：1273-1288,16.DOI:10.3724/SP.J.1004.2013.01273
基于类分布的领域自适应支持向量机
Support Vector Machine for Domain Adaptation Based on Class Distribution
摘要
Abstract
Current domain adaptation methods almost focus on the whole domain sample's distribution and ignore the sample's label information when they consider the distribution discrepancy between source domain and target domain.So,these methods may not work well on imbalanced datasets.In the paper,we employ the proposed distribution discrepancy which considers the sample's label information in the source domain and then propose a novel domain adaptation learning method based on the structure risk minimization principle,called support vector machine for domain adaptation based on class distribution (CDASVM).Accordingly,the CDASVM is extended to MSCDASVM (CDASVM from multiple sources)which can be used to deal with the domain adaptation problem from multiple sources.Experimental results on artificial and real imbalanced datasets show that the proposed machines CDASVM and MSCDASVM outperform or are comparable to the related domain adaption methods.关键词
领域自适应/支持向量机/迁移学习/再生核Hilbert空间Key words
Domain adaptation/support vector machine (SVM)/transfer learning/reproduced kernel Hilbert space引用本文复制引用
应文豪,王士同,邓赵红,王骏..基于类分布的领域自适应支持向量机[J].自动化学报,2013,39(8):1273-1288,16.基金项目
国家自然科学基金(60975027,61170122),江苏省自然科学基金(BK2011003)资助 （60975027,61170122）
Supported by National Natural Science Foundation of China (60975027,61170122),and Natural Science Foundation of Jiangsu Province (BK2011003) （60975027,61170122）
