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Table 1 Algorithm description of train deep belief network.

From: Cough event classification by pretrained deep neural network

Input: Data D = {x}, desired layers K and nodes number for each layer N i
Output: The structure and learned initialization parameters of the DNN.
1. Learn parameters θ1 for the 1st layer RBM from data.
For k = 2:K
2. Initialize the k-th layer RBM by unroll the k-1th layer RBM to the kth layer, of which parameters W k = W k - 1 T
3. Refine the parameters of kth layer RBM from data vectors generated from k-1th layer.
Return: Structure and parameters of the stacked RBMs.
  1. Greedy training process for deep belief network