

lvq2(class)                                  R Documentation

_L_e_a_r_n_i_n_g _V_e_c_t_o_r _Q_u_a_n_t_i_z_a_t_i_o_n _2_._1

_D_e_s_c_r_i_p_t_i_o_n_:

     Moves examples in a codebook to better represent the
     training set.

_U_s_a_g_e_:

     lvq2(x, cl, codebk, niter=10 * n, alpha=0.03, win=0.3)

_A_r_g_u_m_e_n_t_s_:

       x: a matrix or data frame of examples

      cl: a vector or factor of classifications for the
          examples

  codebk: a codebook

   niter: number of iterations

   alpha: constant for training

     win: a tolerance for the closeness of the two nearest
          vectors.

_D_e_t_a_i_l_s_:

     Selects `niter' examples at random  with replacement,
     and adjusts the nearest two examples in the codebook if
     one is correct and the other incorrect.

_V_a_l_u_e_:

     A codebook, represented as a list with components `x'
     and `cl' giving the examples and classes.

_R_e_f_e_r_e_n_c_e_s_:

     Kohonen, T. (1990) The self-organizing map.  Proc. IEEE
     78, 1464-1480.

     Kohonen, T. (1995) Self-Organizing Maps.  Springer,
     Berlin.

_S_e_e _A_l_s_o_:

     `lvqinit', `lvq1', `olvq1', `lvq3', `lvqtest'

_E_x_a_m_p_l_e_s_:

     data(iris3)
     train <- rbind(iris3[1:25,,1],iris3[1:25,,2],iris3[1:25,,3])
     test <- rbind(iris3[26:50,,1],iris3[26:50,,2],iris3[26:50,,3])
     cl <- factor(c(rep("s",25),rep("c",25), rep("v",25)))
     cd <- lvqinit(train, cl, 10)
     lvqtest(cd, train)
     cd0 <- olvq1(train, cl, cd)
     lvqtest(cd0, train)
     cd2 <- lvq2(train, cl, cd0)
     lvqtest(cd2, train)

