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Values of the models parameters are given in Table 8.
R2 values of the models were greater than 0.89.
Table 3 draws a comparison among optimal values of the models.
The probability values of the models were combined using four different consensus methods.
The performance values of the models trained on patent corpora and the tmChem default model are depicted in Fig. 7.
The best classifier for each target was selected based on the AUC values of the models generated.
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Values of the model parameters were determined experimentally.
The calibrated values of the model parameters for the two datasets are summarised in Table 2.
The variation of the design with the assumed values of the model parameters is discussed.
The optimal values of the model parameters were obtained applying the DE (differential evolution) optimization method.
The method is then applied to the third agglomeration with different values of the model parameters.
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