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基于类分布的领域自适应支持向量机

DOI: 10.3724/SP.J.1004.2013.01273, PP. 1273-1288

Keywords: 领域自适应,支持向量机,迁移学习,再生核Hilbert空间

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Abstract:

?现有的领域自适应方法在定义领域间分布距离时,通常仅从领域样本的整体分布上考虑,而未对带类标签的领域样本分布分别进行考虑,从而在一些具有非平衡数据集的应用领域上表现出一定的局限性.对此,在充分考虑源领域样本类信息的基础上,基于结构风险最小化模型,提出了基于类分布的领域自适应支持向量机(Domainadaptationsupportvectormachinebasedonclassdistribution,CDASVM),并将其拓展为可处理多源问题的多源领域自适应支持向量机(CDASVMfrommultiplesources,MSCDASVM),在人造和真实的非平衡数据集上的实验结果表明,所提方法具有优化或可比较的模式分类性能.

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