Bounding Generalization Error Through Bias and Capacity
Bounding Generalization Error Through Bias and Capacity
复制标题
通过偏差和容量限制泛化误差
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
George D. Montañez
中科院分区：
文献类型：
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作者：
Ramya Ramalingam;Nicolas A. Espinosa Dice;Megan L. Kaye;George D. Montañez
我们通过学习算法的能力和归纳偏差的一个向量表示，得到了学习算法的泛化界。利用算法搜索框架，将机器学习转换为一种搜索类型，我们根据偏差的向量表示和假设与数据集之间的互信息，给出了泛化误差上界的统一解释。
We derive generalization bounds on learning algorithms through algorithm capacity and a vector representation of inductive bias. Leveraging the algorithmic search framework, a formalism for casting machine learning as a type of search, we present a unified interpretation of the upper bounds of generalization error in terms of a vector representation of bias and the mutual information between the hypothesis and the dataset.