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English(EN) Transfer Learning for Linear Discriminant Analysis with a Shared Classification Signal

高维分类新迁移学习方法详解

作者 PulseAugur 编辑部 · [2 个来源] ·

本文介绍了一种用于高维二分类线性判别分析的迁移学习新方法。它将每个域中的均值差异分解为共享分类信号和域特定偏差。研究推导了在各种协方差设置下分类误差的确定性极限，量化了共享信号和域特定变化对迁移性能的影响。研究结果还带来了改进的迁移权重和估计器，以及对由不平衡类别样本大小引起的截距偏差的校正。 AI

影响 这项研究可以提高在数据有限或多变场景下分类模型的准确性和效率。

排序理由 该集群包含一篇详细介绍统计学和机器学习新方法的学术论文。

在 arXiv stat.ML 阅读 →

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高维分类新迁移学习方法详解

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yonghan Zhang, Yimeng Fan, Wenya Luo, Jiang Hu ·

    具有共享分类信号的线性判别分析的迁移学习

    arXiv:2607.06936v1 Announce Type: cross Abstract: This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source domains, where the mean difference in each domain is decomposed i…

  2. arXiv stat.ML TIER_1 English(EN) · Jiang Hu ·

    具有共享分类信号的线性判别分析的迁移学习

    This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source domains, where the mean difference in each domain is decomposed into a deterministic common component and a domain-…

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