fdm2id (version 0.9.5)

SVR: Regression using Support Vector Machine

Description

This function builds a regression model using Support Vector Machine.

Usage

SVR(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

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).

kernel

The kernel type.

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, SVRl, SVRr

Examples

# NOT RUN {
require (datasets)
data (trees)
SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1)
SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1)
# }