Learning Vector Quantization 2.1

DESCRIPTION:

Moves examples in a codebook to better represent the training set.

USAGE:

lvq2(x, cl, codebk, niter=100 * nrow(codebk$x), alpha=0.03, win=0.3)

REQUIRED ARGUMENTS:

x
a matrix or data frame of examples
cl
a vector or factor of classifications for the examples
codebk
a codebook

OPTIONAL ARGUMENTS:

niter
number of iterations
alpha
constant for training
win
a tolerance for the closeness of the two nearest vectors.

VALUE:

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

DETAILS:

Selects niter examples at random with replacement, and adjusts the nearest two examples in the codebook if one is correct and the other incorrect.

REFERENCES:

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

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

SEE ALSO:

, , , ,

EXAMPLES:

train <- rbind(iris[1:25,,1],iris[1:25,,2],iris[1:25,,3])
test <- rbind(iris[26:50,,1],iris[26:50,,2],iris[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)