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领域适应核支持向量机

DOI: 10.3724/SP.J.1004.2012.00797, PP. 797-811

Keywords: 领域适应学习,支持向量机,模式分类,最大均值差,最大散度差

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Abstract:

?领域适应学习是一种新颖的解决先验信息缺少的模式分类问题的有效方法,最大化地缩小领域间样本分布差是领域适应学习成功的关键因素之一,而仅考虑领域间分布均值差最小化,使得在具体领域适应学习问题上存在一定的局限性.对此,在某个再生核Hilbert空间,在充分考虑领域间分布的均值差和散度差最小化的基础上,基于结构风险最小化模型,提出一种领域适应核支持向量学习机(Kernelsupportvectormachinefordomainadaptation,DAKSVM)及其最小平方范式,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模式分类性能.

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