banknotes

This data set contains six measurements on 100 genuine and 
100 fradulent old Swiss banknotes

datasets

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
<doi:10.1214/11-AOS889>.

Merlise Clyde

bark

Bayesian Additive Regression Kernels

Zhi Ouyang

Robert Wolpert

banknotes function

a dataframe with the following variables:<dl>
 <dt>Status</dt>
<dd>the status of the banknote: genuine or counterfeit</dd> <dt>Length</dt>
<dd>Length of bill (mm)</dd> <dt>Left</dt>
<dd>Width of left edge (mm)</dd> <dt>Right</dt>
<dd>Width of right edge (mm)</dd> <dt>Bottom</dt>
<dd>Bottom margin width (mm)</dd> <dt>Top</dt>
<dd>Top margin width (mm)</dd> <dt>Diagonal</dt>
<dd>Length of diagonal (mm)</dd>
</dl>

Format

Swiss Bank Notes — banknotes

a dataframe with the following variables:<dl>
 <dt>Status</dt>
<dd>the status of the banknote: genuine or counterfeit</dd>

 <dt>Length</dt>
<dd>Length of bill (mm)</dd>

 <dt>Left</dt>
<dd>Width of left edge (mm)</dd>

 <dt>Right</dt>
<dd>Width of right edge (mm)</dd>

 <dt>Bottom</dt>
<dd>Bottom margin width (mm)</dd>

 <dt>Top</dt>
<dd>Top margin width (mm)</dd>

 <dt>Diagonal</dt>
<dd>Length of diagonal (mm)</dd>


</dl>

banknotes: Swiss Bank Notes

Description

Usage

Arguments

Format