| lvq2 {class} | R Documentation |
Moves examples in a codebook to better represent the training set.
lvq2(x, cl, codebk, niter = 100 * nrow(codebk$x), alpha = 0.03, win = 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 |
win |
a tolerance for the closeness of the two nearest vectors. |
Selects niter examples at random with replacement, and adjusts the nearest
two examples in the codebook if one is correct and the other incorrect.
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, lvq1, olvq1,
lvq3, lvqtest
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)