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Optimized Learning Vector Quantization 1

Usage

olvq1(x, cl, codebk, niter=10 * n, alpha=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

Description

Selects niter examples at random with replacement, and adjusts the nearest example in the codebook for each.

Value

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

References

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

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

See Also

lvqinit, lvqtest, lvq1, lvq2, lvq3

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)
cd1 <- olvq1(train, cl, cd)
lvqtest(cd1, train)