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Jul, 2022
对抗性风险、插值和标签噪声的法则
A law of adversarial risk, interpolation, and label noise
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Daniel Paleka, Amartya Sanyal
TL;DR
本文提出了决策树集成的一种新的方法,将决策树的归一化过程与集成过程相结合,通过交替的方式迭代调整决策树和集成,有效提高了集成的分类性能。
Abstract
In
supervised learning
, it has been shown that
label noise
in the data can be interpolated without penalties on test accuracy under many circumstances. We show that interpolating
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