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Neural network controllers are trained by using dynamic backpropagation algorithm.
Recurrent and back propagation neural networks are trained by using data generated from best response model.
With the training dataset, the deep networks are trained by using a greedy layer-wise training method [32].
For each modality, three feature extraction methods are used and four different classifiers (multilayer perceptron, decision tree, support vector machines, and probabilistic neural network) are trained by using two fusion methods which are matching score level and feature level fusion.
The artificial neural network (ANNs) are trained by using the standard k-fold cross-validation method.
Lastly, an optimal SVM classifier are trained by using grid search technique.
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The ANN model has been trained by using a multi-scale numerical model.
Thus, the model can be trained by using the prepared training set.
For each speaker, only one model is trained by using the speaker's training data.
DNN8 is trained by using the sequential training cost function in Section 4.4.2.
The multilayer perceptron network with a 9-5-1 trainedy was trained by using the back propagation algorithm.
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