The covariance matrix of Green's functions and its application to machine learning

Tomoko Nagai

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arXiv · 2004.06481

The covariance matrix of Green's functions and its application to machine learning

Tomoko Nagai

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Abstract

In this paper, a regression algorithm based on Green's function theory is proposed and implemented. We first survey Green's function for the Dirichlet boundary value problem of 2nd order linear ordinary differential equation, which is a reproducing kernel of a suitable Hilbert space. We next consider a covariance matrix composed of the normalized Green's function, which is regarded as aprobability density function. By supporting Bayesian approach, the covariance matrix gives predictive distribution, which has the predictive mean $\mu$ and the confidence interval [$\mu$-2s, $\mu$+2s], where s stands for a standard deviation.

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Tomoko Nagai. 2020-04-14. The covariance matrix of Green's functions and its application to machine learning. https://arxiv.org/abs/2004.06481

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