Bayesian Additive Regression Kernels (BARK) provides
an implementation for non-parametric function estimation using Levy
Random Field priors for functions that may be represented as a
sum of additive multivariate kernels. Kernels are located at
every data point as in Support Vector Machines, however, coefficients
may be heavily shrunk to zero under the Cauchy process prior, or even,
set to zero. The number of active features is controlled by priors on
precision parameters within the kernels, permitting feature selection. For
more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels",
Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A,
and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy
Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962