论文标题
用于机器学习的准蒙特卡洛数据压缩算法
A quasi-Monte Carlo data compression algorithm for machine learning
论文作者
论文摘要
我们介绍了一种算法，以使用所谓的数字网减少大型数据集，这些网络是单位立方体中分布式的点集。这些点设置与权重（取决于数据集）一起用于表示数据。我们表明，这可用于减少在机器学习算法中查找良好参数所需的计算工作。为了说明我们的方法，我们为神经网络提供了一些数值示例。
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.
