Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo
Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo
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DOI：
10.3389/fbuil.2017.00052
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发表时间：
2017-08
影响因子：
3
通讯作者：
A. B. Abdessalem;N. Dervilis;D. Wagg;K. Worden
中科院分区：
文献类型：
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作者：
A. B. Abdessalem;N. Dervilis;D. Wagg;K. Worden
目前的工作介绍了两种贝叶斯工具的新组合，高斯过程（gp）和近似贝叶斯计算（ABC）算法的使用，用于机器学习应用的核选择和参数估计。本研究论文提出和研究的组合方法提供了使用不同度量和用于贝叶斯回归的核的汇总统计的可能性。所提出的工作向在线、鲁棒、一致和自动化机制迈进了一步，以制定最佳核（甚至平均函数）及其超参数，同时为这些工具用于数学或工程问题（如结构健康监测（SHM）或系统识别（SI））提供置信度评估。
The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs) and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The combined methodology that this research paper proposes and investigates offers the possibility to use different metrics and summary statistics of the kernels used for Bayesian regression. The presented work moves a step towards online, robust, consistent and automated mechanism to formulate optimal kernels (or even mean functions) and their hyperparameters simultaneously offering confidence evaluation when these tools are used for mathematical or engineering problems such as structural health monitoring (SHM) or system identification (SI).