compBoostCMA-methods           package:CMA           R Documentation

_C_o_m_p_o_n_e_n_t_w_i_s_e _B_o_o_s_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Roughly speaking, Boosting combines 'weak learners' in a weighted
     manner in a stronger ensemble.

     'Weak learners' here consist of linear functions in one component
     (variable), as proposed by Buehlmann and Yu (2003).

     It also generates sparsity and can as well be as used for variable
     selection alone. (s. 'GeneSelection'.)

_M_e_t_h_o_d_s:



     _X = "_m_a_t_r_i_x", _y = "_n_u_m_e_r_i_c", _f = "_m_i_s_s_i_n_g" signature 1

     _X = "_m_a_t_r_i_x", _y = "_f_a_c_t_o_r", _f = "_m_i_s_s_i_n_g" signature 2

     _X = "_d_a_t_a._f_r_a_m_e", _y = "_m_i_s_s_i_n_g", _f = "_f_o_r_m_u_l_a" signature 3

     _X = "_E_x_p_r_e_s_s_i_o_n_S_e_t", _y = "_c_h_a_r_a_c_t_e_r", _f = "_m_i_s_s_i_n_g" signature 4

     For further argument and output information, consult
     'compBoostCMA.'

