Xuran Hu1, Mingzhe Zhu1, Zhenpeng Feng2

  • 1School of Electronic Engineering, Xidian University, Xi'an, China; Kunshan Innovation Institute of Xidian University, School of Electronic Engineering, Xidian University, Xi'an, China.

概括

隐藏的SHAP通过解决常常被SHapley添加式扩展 (SHAP) 遗漏的特征相关性来增强可解释的人工智能 (XAI). 这种新的方法提高了网络解释的准确性,并减少了高维数据的复杂性.

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Collisions in Multiple Dimensions: Problem Solving01:06

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Outliers and Influential Points01:08

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