fdm2id (version 0.9.5)

SVRl: Regression using Support Vector Machine with a linear kernel

Description

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

Usage

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

Arguments

x

Predictor matrix.

y

Response vector.

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.

Value

The classification model.

See Also

svm, SVR

Examples

# NOT RUN {
require (datasets)
data (trees)
SVRl (trees [, -3], trees [, 3], cost = 1)
# }