Ensemble Deep Learning for Biomedical Time Series Classification

Algorithm 2

Pseudocode of implicit training.

Algorithm]: Implicit Training

Input]: Training Samples , ),  

Output]: An DNN Model

Begin

 Best = 0

 while (!StopCondition)

  dW =  //Initialize the weight changes

  Matrix =  //Initialize the confusion matrix

  for    to  N

    = LocalViewTransform(, rand) //Perform the local view transformation (start from a random position)

    = DistortedViewTransform(, rand) //Perform the distorted view transformation with high probability

   dW = dW + BackPropagation(, ) //Invoke Backpropagation and accumulate

   if ( %   == 0) // training samples have been presented to the network

    UpdateNN(dW) //Adjust the weights

    dW =

    for    to   //Training samples used between two adjacent weight-updating processes

      = LocalViewTransform (,

                     fixed) //Perform the local view transformation (start from a particular position)

     Matrix = Matrix + Test(, ) //Test the current DNN model

    end

   end

  end

  if (Performance(Matrix) > Best)

   Best = Performance(Matrix)

   SaveNN(Model)

  end

 end

End