We show that Gaussian process regression (GPR) allows representating multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster, much simpler to use, and more accurate.

翻译：我们发现高斯进程回归(GPR)允许通过内核设计代表低维术语的多变量功能。 当使用高维模型代表法(高维模型代表法)建造的内核时,人们获得与先前提议的HDMR-GPR计划相似的表述方式,同时速度更快,使用更简单,更准确。