lvq2 {class} | R Documentation

## 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.

### 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.

### 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`, `lvq1`, `olvq1`, `lvq3`, `lvqtest`

### Examples

    data(iris3)
    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)

[Package class version 7.2-30 Index]