Moves examples in a codebook to better represent the training set.
lvq3(x, cl, codebk, niter = 100*nrow(codebk$x), alpha = 0.03,
win = 0.3, epsilon = 0.1)
a matrix or data frame of examples
a vector or factor of classifications for the examples
a codebook
number of iterations
constant for training
a tolerance for the closeness of the two nearest vectors.
proportion of move for correct vectors
A codebook, represented as a list with components x
and cl
giving the examples and classes.
Selects niter
examples at random with replacement, and adjusts the nearest
two examples in the codebook for each.
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.
# NOT RUN { 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) # }