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核分布一致局部领域适应学习

DOI: 10.3724/SP.J.1004.2013.01295, PP. 1295-1309

Keywords: 领域适应学习,核分布一致,局部学习,模式分类,最大平均差

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Abstract:

?针对领域适应学习(Domainadaptationlearning,DAL)问题,提出一种核分布一致局部领域适应学习机(Kerneldistributionconsistencybasedlocaldomainadaptationclassifier,KDC-LDAC),在某个通用再生核Hilbert空间(UniversallyreproducedkernelHilbertspace,URKHS),基于结构风险最小化模型,KDC-LDAC首先学习一个核分布一致正则化支持向量机(Supportvectormachine,SVM),对目标数据进行初始划分;然后,基于核局部学习思想,对目标数据类别信息进行局部回归重构;最后,利用学习获得的类别信息,在目标领域训练学习一个适于目标判别的分类器.人造和实际数据集实验结果显示,所提方法具有优化或可比较的领域适应学习性能.

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