| lvq1 {class} | R Documentation |
Learning Vector Quantization 1
Description
Moves examples in a codebook to better represent the training set.
Usage
lvq1(x, cl, codebk, niter = 100 * nrow(codebk$x), alpha = 0.03)
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 |
Details
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.
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, olvq1, lvq2, 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)
cd1 <- lvq1(train, cl, cd0)
lvqtest(cd1, train)