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DESCRIPTION
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DESCRIPTION
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Package: gpHist
Type: Package
Title: Gaussian Process with Histogram Intersection Kernel
Version: 0.1
Date: 2017-11-24
Author: Dennis Becker
Maintainer: Dennis Becker <dbecker@leuphana.de>
Description: 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>.
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2017-11-24 13:19:29 UTC; dennis
Repository: CRAN
Date/Publication: 2017-11-24 14:03:57 UTC