olvq1 {class}R Documentation

Optimized Learning Vector Quantization 1


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


olvq1(x, cl, codebk, niter = 40 * nrow(codebk$x), alpha = 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


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


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.

See Also

lvqinit, lvqtest, lvq1, lvq2, lvq3


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)

[Package class version 7.2-23 Index]