class (version 7.3-0)

lvq2: 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)

Arguments

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.

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.

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.

See Also

lvqinit, lvq1, olvq1, lvq3, lvqtest

Examples

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)