lvq1                  package: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 _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:

     lvq1(x, cl, codebk, niter = 100 * nrow(codebk$x), alpha = 0.03)

_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 

_D_e_t_a_i_l_s:

     Selects 'niter' examples at random  with replacement, and adjusts
     the nearest example in the codebook for each.

_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.

     Ripley, B. D. (1996) _Pattern Recognition and Neural Networks._
     Cambridge.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'lvqinit', 'olvq1', 'lvq2', '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)
     cd1 <- lvq1(train, cl, cd0)
     lvqtest(cd1, train)

