| olvq1 {class} | R Documentation |
Moves examples in a codebook to better represent the training set.
olvq1(x, cl, codebk, niter = 40 * nrow(codebk$x), alpha = 0.3)
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 |
Selects niter examples at random with replacement, and adjusts the
nearest example in the codebook for each.
A codebook, represented as a list with components x and cl giving
the examples and classes.
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.
lvqinit, lvqtest, lvq1, lvq2, lvq3
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)
cd1 <- olvq1(train, cl, cd)
lvqtest(cd1, train)