一种用于机器学习的拟蒙特卡罗方法数据压缩算法
A quasi-Monte Carlo data compression algorithm for machine learning
摘要：
我们介绍了一种算法来减少大型数据集使用所谓的数字网，这是分布良好的点集的单位立方体。这些点集与取决于数据集的权重一起用于表示数据。我们表明，这可以用来减少在机器学习算法中找到好的参数所需的计算工作量。为了说明我们的方法，我们提供了一些神经网络的数值例子。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
