gater: Gater function for mixture SVMs

In SwarmSVM: Ensemble Learning Algorithms Based on Support Vector Machines

Gater function for mixture SVMs

Description

Gater function for mixture SVMs

Usage

gater(
  x,
  y,
  S,
  hidden,
  learningrate = 0.01,
  threshold = 0.01,
  stepmax = 100,
  verbose = verbose,
  ...
)

Arguments

x	the nxp training data matrix. Could be a matrix or a sparse matrix object.
y	a response vector for prediction tasks with one value for each of the n rows of x. For classification, the values correspond to class labels and can be a 1xn matrix, a simple vector or a factor. For regression, the values correspond to the values to predict, and can be a 1xn matrix or a simple vector.
S	the prediction matrix from experts
hidden	the number of neurons in the hidden layer
learningrate	the learningrate for the back propagation
threshold	neural network stops training once all gradient is below the threshold
stepmax	the maximum iteration of the neural network training process
verbose	a logical value indicating whether to print information of training.
...	other parameters passing to neuralnet

SwarmSVM documentation built on Dec. 28, 2022, 1:24 a.m.