gpHist: Gaussian Process with Histogram Intersection Kernel

Provides an implementation of a Gaussian process regression with a histogram intersection kernel (HIK) and utilizes approximations to speed up learning and prediction. In contrast to a squared exponential kernel, an HIK provides advantages such as linear memory and learning time requirements. However, the HIK only provides a piecewise-linear approximation of the function. Furthermore, the number of estimated eigenvalues is reduced. The eigenvalues and vectors are required for the approximation of the log-likelihood function as well as the approximation of the predicted variance of new samples. This package provides approximations for a single eigenvalue as well as multiple. Further information of the variance and log-likelihood approximation, as well as the Gaussian process with HIK, can be found in the paper by Rodner et al. (2016) <doi:10.1007/s11263-016-0929-y>.

Package details

AuthorDennis Becker
MaintainerDennis Becker <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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gpHist documentation built on Nov. 24, 2017, 5:03 p.m.