The inputs of the threenorm problem are points from two Gaussian
distributions with unit covariance matrix. Class 1 is drawn with
equal probability from a unit multivariate normal with mean
$(a,a,\ldots,a)$ and from a unit multivariate normal with mean
$(-a,-a,\ldots,-a)$. Class 2 is drawn from a multivariate normal
with mean at $(a,-a,a, \ldots,-a)$, $a=2/d^{-0.5}$.
Usage
mlbench.threenorm(n, d=20)
Arguments
n
number of patterns to create
d
dimension of the threenorm problem
Value
Returns an object of class "mlbench.threenorm" with components
xinput values
classesfactor vector of length n with target classes
References
Breiman, L. (1996). Bias, variance, and arcing classifiers.
Tech. Rep. 460, Statistics Department, University of California,
Berkeley, CA, USA.