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In data analytics, training of the model is typically done offline and it usually takes a significant amount of time.
Multi-layer perceptron neural network structure and back-propagation algorithm are used for the training of the model.
Training of the model was conducted for the years 1991 1995, where the growth factor (found in Equation 3) was fitted to the level 1 complexity.
The training of the model is carried out in the low-dimensional space, and the inference in the high-dimensional log-spectral domain.
Fuzzy models can make use of this situation as fuzzy rules can be based upon the qualitative information that is found in the literature whereas training of the model can be performed with data that is available.
With the growing trend for machine-learning approaches, a large-scale dataset is essential to enable efficient training of the model, and it is also required for statistically reliable performance evaluation.
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One approach could be using vocals separated from polyphonic music for training of the models.
For training of the models, genes upregulated more than twofold by treatment were used as positive examples.
The data sets have been composed from QSAR data sets from the literature for the training of the models and the DUD data sets for virtual screening for the respective targets.
Two hundred and fifty-two patient records were used for training of the models and 100 were used for validation.
252 patient records were used for training of the models and 100 were used for validation.
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