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可迁移测度准则下的协变量偏移修正多源集成方法

杨兴明 吴克伟 孙永宣 谢昭

杨兴明, 吴克伟, 孙永宣, 谢昭. 可迁移测度准则下的协变量偏移修正多源集成方法[J]. 电子与信息学报, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
引用本文: 杨兴明, 吴克伟, 孙永宣, 谢昭. 可迁移测度准则下的协变量偏移修正多源集成方法[J]. 电子与信息学报, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
Yang Xing-ming, Wu Ke-wei, Sun Yong-xuan, Xie Zhao. Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
Citation: Yang Xing-ming, Wu Ke-wei, Sun Yong-xuan, Xie Zhao. Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323

可迁移测度准则下的协变量偏移修正多源集成方法

doi: 10.11999/JEIT150323
基金项目: 

国家自然科学基金(60905005, 61273237)

Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric

Funds: 

The National Natural Science Foundation of China (60905005, 61273237)

  • 摘要: 迁移学习通过充分利用源域共享知识,实现对目标域的小样本问题求解,然而,对训练和测试样本分布差异测度仍然是该领域的主要挑战。该文针对多源迁移学习算法中,由于源域选择和源域辅助样本选择不当引起的负迁移问题进行研究,提出一种可迁移测度准则下的协变量偏移修正多源集成方法。首先,根据源域和目标域之间的协变量偏移原则,利用联合概率的密度估计,定义辅助样本的可迁移测度,验证目标域和源域在数据空间中标记分布的一致性。其次,在多源域选择阶段,引入非迁移判别过程,提高了源域知识的迁移准确性。最后,在Caltech 256数据集中,验证了Gist特征知识表示和迁移的有效性,分析了多种条件下的辅助样本选择和源域选择的有效性。实验结果表明所提算法可有效降低负迁移现象的发生,获得更好的迁移学习性能
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出版历程
  • 收稿日期:  2015-03-17
  • 修回日期:  2015-08-13
  • 刊出日期:  2015-12-19

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