

   LLeeaarrnniinngg VVeeccttoorr QQuuaannttiizzaattiioonn 33

        lvq3(x, cl, codebk, niter=10*n, alpha=0.03, win=0.3, epsilon=0.1)

   AArrgguummeenntts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.

    epsilon: proportion of move for correct vectors

   DDeessccrriippttiioonn::

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

   VVaalluuee::

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

   RReeffeerreenncceess::

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

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

   SSeeee AAllssoo::

        `lvqinit', `lvq1', `olvq1', `lvq2', `lvqtest'

   EExxaammpplleess::

        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)
        cd3 <- lvq3(train, cl, cd0)
        lvqtest(cd3, train)

