fdm2id (version 0.9.6)

MLP: Classification using Multilayer Perceptron

Description

This function builds a classification model using Multilayer Perceptron.

Usage

MLP(
  train,
  labels,
  hidden = ifelse(is.vector(train), 2:(1 + nlevels(labels)), 2:(ncol(train) +
    nlevels(labels))),
  decay = 10^(-3:-1),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Value

The classification model.

Arguments

train

The training set (description), as a data.frame.

labels

Class labels of the training set (vector or factor).

hidden

The size of the hidden layer (if a vector, cross-over validation is used to chose the best size).

decay

The decay (between 0 and 1) of the backpropagation algorithm (if a vector, cross-over validation is used to chose the best size).

methodparameters

Object containing the parameters. If given, it replaces size and decay.

tune

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

...

Other parameters.

See Also

nnet

Examples

if (FALSE) {
require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)
}