fdm2id (version 0.9.9)

SVRr: Regression using Support Vector Machine with a radial kernel

Description

This function builds a regression model using Support Vector Machine with a radial kernel.

Usage

SVRr(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

Value

The classification model.

Arguments

x

Predictor matrix.

y

Response vector.

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).

epsilon

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

params

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

tune

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

...

Other arguments.

See Also

svm, SVR

Examples

if (FALSE) {
require (datasets)
data (trees)
SVRr (trees [, -3], trees [, 3], gamma = 1, cost = 1)
}