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AdaBoost with Haar-like features was trained following [22].

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Then, the RBM2 with these initial features is trained with the labeled training data.

In the training stage, DAE and BF-DNN models for feature transformation and speaker models with transformed features are trained.

A logistic regression classifier with appropriate features is trained to recognize reliable dependency arcs (correct with high precision).

Each of the extracted features is trained by using two neural networks for each type of emotion.

Next, the facial features are trained according to a specific classifier in order to determine explicitly distinctive boundaries between emotions.

These features are trained using Support Vector Machine SVMM) with radial basis function kernel and the developed classifier is able to discriminate significantly between 20 patients affected from ischemic stroke and 25 healthy controls.

Models based on these individual features were trained using the scheme described above.

For further assessing the effects of predicted RC on prediction of therapy outcome, linear models for the prediction of changes in VL or CD4+ T-cell count based on the following sets of input features were trained: (i) Treatment activity expressed in terms of TAS.

In each iteration, an SVM-based model on selected features is trained.

The features are trained on manually curated nucleolar datasets (listed in Additional File 1) and a randomly generated non-nucleolar dataset.

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