《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (1): 35-45.doi: 10.6040/j.issn.1671-9352.4.2022.606
杨梅1,3,4(
),邓雯1,张本文2,闵帆1,3,4,*()
Mei YANG1,3,4(),Wen DENG1,Benwen ZHANG2,Fan MIN1,3,4,*()
摘要：
提出了多示例嵌入学习(multi-instance learning, MIL)的实例关联性挖掘与强化算法(multi-instance embedding learning with instance affinity mining and reinforcement, MEMR), 包括3个技术。关联性挖掘技术基于自定义的关联性指标, 首先在负实例空间中选择初始负代表实例集, 然后根据正、负实例间的差异性, 选择初始正代表实例集。关联性强化技术分别评估初始正、负代表实例集与整个实例空间的正负关联性, 获得整体关联性更强的代表实例集。包嵌入技术通过嵌入函数将包转换为单向量进行学习。实验在4类应用领域和7种对比算法上进行。结果表明, MEMR的准确性总体优于其他对比算法, 特别是在图像检索和网页推荐数据集上具有显著优势。
中图分类号:
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