All decision parameters are trained using the training set, while all the experimental results are obtained using the test set.
A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India.
In the PoPMuSiC method (Dehouck et al., 2009), the estimated stability change on mutation is expressed as a linear combination of 26 different energy functions, whose parameters were trained using an artificial neural network.
All the autoencoders were trained using the sparsity parameter ρ = 0.03, sparsity weight β = 5, and the weight decay parameter λ = 0.003.
In a conventional DAE, parameters are trained using pairs of reverberant speech and clean speech under various acoustic conditions (that is, an environment-independent DAE).
For a conventional DAE (environment-independent DAE), parameters are trained using pairs of reverberant speech and clean speech under various acoustic conditions.
The parameters of the emission probabilities are trained using the maximum likelihood criterion and the training procedure is initialized by a linear segmentation.
Thus, a specific classifier was trained using the same training parameters as the cascade, except that the positive images were extracted from the FERET database.
First a three-layer, feed-forward back-propagation neural network having nine input parameters, twenty-five hiddeneuronsns and one output parameter was trained using 116 experimental and monitored blast records from one of the most important copper mines in Iran.
The parameters in the prediction model were trained using a set of experimentally validated mature miRNAs in the miRBase and further evaluated using a dataset that does not overlap with the training dataset.
