fdm2id (version 1.0.1)

SVM: 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"),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Value

The classification model.

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.

nfolds

The number of folds of the cross-validation a method runs to choose its hyperparameters. Only used when there is something to choose, i.e. when one of them is given as a vector. Lower it to fit faster, at the cost of a noisier choice.

tune

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

methodparameters

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

graph

Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: SVM does not produce a plot.

seed

A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.

...

Other arguments.

See Also

svm, SVMl, SVMr

Examples

if (FALSE) {
require (datasets)
data (iris)
SVM (iris [, -5], iris [, 5], kernel = "linear", cost = 1)
SVM (iris [, -5], iris [, 5], kernel = "radial", gamma = 1, cost = 1)
}