Guest Editorial: Regularisation Methods in Regression and Classification

Gerhard Tutz

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Guest Editorial: Regularisation Methods in Regression and Classification

机译：客座社论：回归和分类中的正则化方法

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Regularisation has become an important tool in statistical modelling. In particular the challenge of high dimensional data boosted the fitting of more and more complex models that can not be fitted without appropriate regularisation. The need for regularisation, however, is not restricted to the modelling of high dimensional data, it is mainly driven by the complexity of the model. When the model includes non-parametric function estimation regularisation restricts the class of functions that are fitted.

机译：正则化已成为统计建模中的重要工具。特别是高维数据的挑战推动了越来越多的复杂模型的拟合，这些模型如果没有适当的规范化就无法拟合。但是，对正则化的需求不仅限于高维数据的建模，它主要是由模型的复杂性驱动的。当模型包含非参数函数估计时，正则化会限制所拟合函数的类别。

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《Statistics and computing》 |2010年第2期|p.117-118|共2页
作者
Gerhard Tutz;
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Ludwig-Maximilians-Universitaet Muenchen, Akademiestrasse 1, 80799 Munich, Germany;

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Guest Editorial: Regularisation Methods in Regression and Classification

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