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【24h】Enhancing the Adaptiveness of Gaussian Process Regression based on Power Spectral Density

机译基于功率谱密度提高高斯过程回归的自适应性

【摘要】 In many years, Gaussian process was popularly utilized in many research areas such as signal processing, data communications and image processing, etc. Unlike other techniques which try to determine all of the parameters of system model, Gaussian process adapts these parameters to reflect the actual underlying model. Because of that, this approach can be explicitly addressed as a non-parametric methodology. As a comparison to other well-known methods, Gaussian process regression (GPR) possesses much better performance in terms of precision and versatility. However, this technique does have some drawbacks. One of them is the adaptiveness to the complex data. In this research, we would like to introduce a novel solution based on power spectral density to adapt the model for better accuracy.

【摘要机译】多年以来,高斯过程在许多研究领域得到了广泛应用,例如信号处理,数据通信和图像处理等。与试图确定系统模型的所有参数的其他技术不同,高斯过程通过适应这些参数来反映实际情况。基础模型。因此,该方法可以明确地作为非参数方法论来解决。与其他众所周知的方法相比,高斯过程回归(GPR)在精度和通用性方面拥有更好的性能。但是,这种技术确实有一些缺点。其中之一是对复杂数据的适应性。在这项研究中,我们想介绍一种基于功率谱密度的新颖解决方案,以使模型适应更高的精度。

【作者】Dinh-Mao Bui; Nguyen Anh Tu; Kok-Seng Wong;

【作者单位】Nazarbayev University Department of Computer Science Nur-Sultan Kazakhstan;

【年(卷),期】2020(),

【年度】2020

【页码】1-6

【总页数】6

【正文语种】

【中图分类】;

【关键词】Kernel; Gaussian processes; Frequency-domain analysis; Adaptation models; Ground penetrating radar; Data models; Covariance matrices;

机译 核心;高斯过程;频域分析;适应模型;探地雷达;数据模型;协方差矩阵;
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