We introduce an algorithm to reduce large data sets using so-called digital nets, which are well distributed point sets in the unit cube. These point sets together with weights, which depend on the data set, are used to represent the data. We show that this can be used to reduce the computational effort needed in finding good parameters in machine learning algorithms. To illustrate our method we provide some numerical examples for neural networks.

翻译：我们引入了一种算法,用所谓的数字网来减少大型数据集,这些算法是单位立方体中分布良好的点数组。这些点数组和重数组,取决于数据集,用来代表数据。我们表明,这可以用来减少在机器学习算法中寻找良好参数所需的计算努力。为了说明我们的方法,我们为神经网络提供了一些数字例子。