sim_circle

The classification problem Circle is described in the BARK paper (2008).
Inputs are dim independent variables uniformly
distributed on the interval $[-1,1]$, only the first 2
out of these dim are actually signals.
Outputs are created according to the formula
$$y = 1(x1^2+x2^2 \le 2/\pi)$$

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

sim_circle function

<dl><dt>n</dt>
<dd>number of data points to generate</dd>
<dt>dim</dt>
<dd>number of dimension of the problem, no less than 2</dd></dl>

Arguments

Simulate Data from Hyper-Sphere for Classification Problems — sim_circle

<dl>

<dt>n</dt>
<dd>number of data points to generate</dd>


<dt>dim</dt>
<dd>number of dimension of the problem, no less than 2</dd>

</dl>

sim_circle: Simulate Data from Hyper-Sphere for Classification Problems

Description

Usage

Value

Arguments

References

See Also

Examples