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, 14641480.
Kohonen, T. (1995) Self-Organizing Maps. Springer, Berlin.
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)