SVM {fdm2id}

Classification using Support Vector Machine

Description

This function builds a classification model using Support Vector Machine.

Usage

SVM(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Arguments

train

The training set (description), as a data.frame.

labels

Class labels of the training set (vector or factor).

gamma

The gamma parameter (if a vector, cross-over validation is used to chose the best size).

cost

The cost parameter (if a vector, cross-over validation is used to chose the best size).

kernel

The kernel type.

methodparameters

Object containing the parameters. If given, it replaces gamma and cost.

tune

If true, the function returns paramters instead of a classification model.

...

Other arguments.

Value

The classification model.

See Also

svm, SVMl, SVMr

Examples

## Not run: 
require (datasets)
data (iris)
SVM (iris [, -5], iris [, 5], kernel = "linear", cost = 1)
SVM (iris [, -5], iris [, 5], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)