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In ANN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta (EDBD).
Among the LSTAR-GARCH-NN models, the training and test MSE errors are calculated comparatively lower for the LSTAR-LST-GARCH-MLP models.
In the models, the training and testing results have shown that ANN and FL methods have strong potential for predicting the fc and Ed of crushed tile concretes exposed to elevated temperatures.
With detector models, the training data can be utilised more efficiently than with combination models, because all combinations containing the target drum can be used to train the model.
Using multivariate generalised estimating equation regression models, the training dataset enabled generation of patient-risk and surgeon-experience adjustment factors.
We used a 50percentt random sample of nursing homes to fit the models (the "training" sample) and saved the remaining data (the "test" sample) for model validation.
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In addition, for the purpose of training our model, the training dataset does not contain any uncharacterized proteins.
Firstly, this approach calculates the supply of physicians through the usual process of modelling the training of physicians.
Based on this procedure, the performance of the models outside the training location cannot be inferred.
We train the pairwise models on the training data and obtain predictions on R t.
Table 3 contains the summary of evaluations of the prediction models using the training set TS270.
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