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These possible AU linear dependencies will be determined through training the FFNNs and the AUs in this situation will be treated as a single input.
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In the first and fourth layers, the nodes are adaptive and some used parameters in these nodes are determined through training phase, whereas the nodes in other layers are fixed.
In fact, in practical applications, the optimal dimension can be determined through internal multiple-fold cross-validation of the training sets.
External validation will be determined through the application of the model to the testing data, and internal validation through comparison to the original training data.
Ideally, it should be determined through a lab test.
"The question of legality will only be determined through discovery".
UAV control may be determined through democratic means.
What's important can be determined through eye tracking.
This is determined through the training process.
The appropriate architecture of the neural network models was determined through several steps of training and testing of the models.
The number of hidden layers, neurons in each hidden layer and training parameters were determined through trial and error to be optimal.
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