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调节极化配件:BLINEX-Pcomp具有不对称的风险处罚,用于强大的Pcomp分类
Long Tang1, Xin Si1, Yingjie Tian2
1School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China.
概括
这项研究引入了BLINEX-Pcomp用于Pcomp分类,降低了订购双对样本的注释成本. 该模型通过对正面和负面风险施加不同的处罚来平衡过度装配和不足装配风险.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- Pcomp分类为不准确监督的学习提供了一个新的范式.
- 由于对经验风险标志差异的处理不充分,现有的方法在两极化拟合方面扎.
研究的目的:
- 提出一个新的模型,BLINEX-Pcomp,它解决了Pcomp分类中的局限性.
- 为了改善对照样本学习中过度配套和不足配套风险之间的平衡.
主要方法:
- 引入了BLINEX-Pcomp模型,使用有限的线性指数函数来对差异化风险处罚.
- 开发了一个多视图版本 (MV-BLINEX-Pcomp) 集成多视图功能.
- 设计了一个双阶段的解决方案,用于训练MV-BLINEX-Pcomp模型.
主要成果:
- BLINEX-Pcomp有效地平衡了对等水平的风险,将重点转移到具有挑战性的样本上.
- 通过结合多视图功能,MV-BLINEX-Pcomp表现出增强的性能.
- 理论验证证实MV-BLINEX-Pcomp降解为具有单视图特征的BLINEX-Pcomp.
结论:
- 拟议的BLINEX-Pcomp和MV-BLINEX-Pcomp模型为Pcomp分类提供了有效的解决方案.
- 这些方法成功地解决了与双向学习中的经验风险差异相关的挑战.
- 数字结果验证了在比较实验中提出的方法的优越性.
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