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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,
  ...
)

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.

Value

The classification model.

See Also

Examples

## Not run: 
require (datasets)
data (trees)
SVRr (trees [, -3], trees [, 3], gamma = 1, cost = 1)

## End(Not run)

fdm2id

Data Mining and R Programming for Beginners

v0.9.5
GPL-3
Authors
Alexandre Blansché [aut, cre]
Initial release

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