fdm2id: SVMr – R documentation

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SVMr

Classification using Support Vector Machine with a radial kernel

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

SVMr(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

`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).
`methodparameters`	Object containing the parameters. If given, it replaces `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

The classification model.

## Not run: 
require (datasets)
data (iris)
SVMr (iris [, -5], iris [, 5], gamma = 1, cost = 1)

## End(Not run)

Data Mining and R Programming for Beginners

v0.9.5

GPL-3

Authors

Alexandre Blansché [aut, cre]

Initial release