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扭曲密度比率估计方法

Zi-En Fan1, Feng Lian1, Xin-Ran Li1

  • 1School of Automation Science and Technology, Xi'an Jiaotong University, No.28, West Xianning Road, Xi'an, 710049, Shaanxi, China.

Neural networks : the official journal of the International Neural Network Society
|December 23, 2025
PubMed
概括
此摘要是机器生成的。

扭曲密度比率估计 (WDRE) 使用规范化流量来估计概率密度比率. 这种新的方法通过转换分布来简化复杂比率估计,提高机器学习任务的准确性.

关键词:
密度比率估计的估计密度比率.相互信息估计的估计.规范化流量的流量.

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科学领域:

  • 机器学习 机器学习
  • 统计推理 统计推理
  • 可能性理论概率理论.

背景情况:

  • 估计概率密度比率 (p/q) 在机器学习中至关重要.
  • 基于标准分类的方法在有很大的差异的分布上扎.
  • 现有的中间分销策略复杂,缺乏明确的准确性指导方针.

研究的目的:

  • 提出一种新的方法,即扭曲密度比率估计 (WDRE),用于准确估计密度比率.
  • 在处理高差异分布时解决当前方法的局限性.
  • 为密度比率估计提供更有效,更准确的方法.

主要方法:

  • 开发了使用规范化流量的扭曲密度比率估计 (WDRE).
  • 将原始密度比重重构成两个更简单的比率的乘积,减少差异.
  • 应用了转换来推动分布向分析可处理的目标.

主要成果:

  • WDRE有效地估计了密度比率,即使对于有很大的差异的分布.
  • 该方法避免了多个中间分布的需要.
  • 通过在数据和潜伏空间中适用,WDRE表现出灵活性.

结论:

  • 在密度比率估计方面,WDRE提供了显著的进步.
  • 基于规范流量的方法提高了准确性,并简化了估计过程.
  • 实验结果验证了WDRE在合成和现实数据集上的有效性.