Abstract
Support vector regression (SVR) is a powerful learning technique in the framework of statistical learning theory, while Kriging is a well-entrenched prediction method traditionally used in the spatial statistics field. However, the two techniques share the same framework of reproducing kernel Hilbert space. In this paper, we first review the formulations of SILF-SVR where soft insensitive loss function is utilized and ordinary Kriging, and then prove the equivalence between the two techniques under the assumption that the kernel function is substituted by covariance function.
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An, W., Sun, Y. An Equivalence between SILF-SVR and Ordinary Kriging. Neural Process Lett 23, 133–141 (2006). https://doi.org/10.1007/s11063-005-4015-7
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DOI: https://doi.org/10.1007/s11063-005-4015-7