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- 2015
基于平移不变核的异构迁移学习
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Abstract:
摘要 提出一种新的异构迁移学习方法.利用与目标数据集相关的异构特征数据集.通过把目标集和异构集的数据使用平移不变核(欧式距离核和径向基函数核),映射到一个新的再生核希尔伯特空间上.在新空间中2个数据集的特征相同,特征维度相等,分布接近,且保持数据的拓扑性质不变.实验证明,该方法特别是基于欧式距离核的方法取得了较好的效果,在目标训练集的标注数据较少时,有大于5%甚至超过10%的精度提高.
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