mlbench.threenorm          package:mlbench          R Documentation

_T_h_r_e_e_n_o_r_m _B_e_n_c_h_m_a_r_k _P_r_o_b_l_e_m

_D_e_s_c_r_i_p_t_i_o_n:

     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,...,a) and from a unit multivariate normal with mean 
     (-a,-a,...,-a). Class 2 is drawn from a multivariate normal with
     mean at (a,-a,a, ...,-a), a=2/d^{-0.5}.

_U_s_a_g_e:

     mlbench.threenorm(n, d=20)

_A_r_g_u_m_e_n_t_s:

       n: number of patterns to create

       d: dimension of the threenorm problem

_V_a_l_u_e:

     Returns an object of class '"mlbench.threenorm"' with components 

       x: input values

 classes: factor vector of length 'n' with target classes

_R_e_f_e_r_e_n_c_e_s:

     Breiman, L. (1996). Bias, variance, and arcing classifiers. Tech.
     Rep. 460, Statistics Department, University of California,
     Berkeley, CA, USA.

_E_x_a_m_p_l_e_s:

     p<-mlbench.threenorm(1000, d=2)
     plot(p)

