Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that is capable of dealing with high-dimensional and huge datasets - and enjoys typical standard machine learning approximation properties. We extend the SDKN to combine it with standard machine learning modules and compare it with Neural Networks on the scientific challenge of data-driven prediction of closure terms of turbulent flows. We show experimentally that the SDKNs are capable of dealing with large datasets and achieve near-perfect accuracy on the given application.

翻译：处理大型数据集时,机器学习的标准内核方法通常会挣扎。我们审查了最近采用的一种结构式深内核网络(SDKN)方法,它能够处理高维和巨大的数据集,并具有典型的标准机器学习近似特性。我们扩大了SDKN,把它与标准的机器学习模块结合起来,并与神经网络比较,研究数据驱动预测动荡流动封闭条件的科学挑战。我们实验性地表明,SDKN有能力处理大型数据集并实现对特定应用的近乎完美准确性。