标题： 通过异方差理性克里金法的主动学习 显示英文标题

标题： Active Learning via Heteroskedastic Rational Kriging

摘要： 主动学习方法用于模拟依赖于平稳高斯过程的复杂计算机模型，通常会产生均匀填充整个实验区域的设计点，这对于仅在小区域内变化的函数来说可能是浪费的。 在本文中，我们提出了一种新的高斯过程模型，该模型能够捕捉函数的异方差性。 使用此新模型的主动学习可以将设计点放置在响应面的更有趣区域，从而获得更准确的代理模型。 所提出的主动学习方法通过模拟和两个真实数据集与最先进的方法进行了比较。 结果表明，其性能与其他非平稳高斯过程方法相当或更好，但快了几个数量级。 显示英文摘要

摘要： Active learning methods for emulating complex computer models that rely on stationary Gaussian processes tend to produce design points that uniformly fill the entire experimental region, which can be wasteful for functions which vary only in small regions. In this article, we propose a new Gaussian process model that captures the heteroskedasticity of the function. Active learning using this new model can place design points in the more interesting regions of the response surface, and thus obtain surrogate models with better accuracy. The proposed active learning method is compared with the state-of-the-art methods using simulations and two real datasets. It is found to have comparable or better performance relative to other non-stationary Gaussian process-based methods, but faster by orders of magnitude.