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On the Selection of the Regularization Parameter in Stacking
Neural Processing Letters ( IF 3.1 ) Pub Date : 2020-10-23 , DOI: 10.1007/s11063-020-10378-6
Tadayoshi Fushiki

Stacking is a model combination technique to improve prediction accuracy. Regularization is usually necessary in stacking because some predictions used in the model combination provide similar predictions. Cross-validation is generally used to select the regularization parameter, but it incurs a high computational cost. This paper proposes two simple low computational cost methods for selecting the regularization parameter. The effectiveness of the methods is examined in numerical experiments. Asymptotic results in a particular setting are also shown.



中文翻译:

堆叠中正则化参数的选择

堆叠是一种模型组合技术,可以提高预测准确性。正则化通常在堆栈中是必需的,因为模型组合中使用的某些预测提供了相似的预测。交叉验证通常用于选择正则化参数,但会产生较高的计算成本。本文提出了两种简单的低计算成本的方法来选择正则化参数。在数值实验中检验了该方法的有效性。还显示了特定设置下的渐近结果。

更新日期:2020-10-30
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