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We determined these values for both models using the nine data sets.
Then checking the predictive power of both models using the developed database.
As such, we evaluate both models using the raw binary matrix (SU) and the IDF-transormed matrix (IDF _SU).
For the three pairs of variables, we have compared both models using the Akaike information criterion (AIC), defined by (Akaike 1974) AIC = - 2 log L + 2 d ; (21).
In our scheme the shared variable is alternately updated by the connecting submodels, with both models using the same combined storage relationship.
Predictions for further dipole geometries and a sorbing tracer have been calculated by means of both models using the flow and transport parameters deduced from fits for a single dipole experiment.
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Both models use the attribution of the medical specialities based on the characteristics of their professional activities.
Both models use the UK formulation to take advantage of potential relationships between the response variables and available trend variables.
Both models use the idea of a 'trace' left by presynaptic spikes (see previous section), but in slightly different formulations.
In both models used, the Col-Treg cells are detected in inflamed joints and axillary LNs following injection.
Both models use the same set of final variables, however the odds ratios are larger for the model trained on Kalman filter estimates than the odds ratios for the model trained on raw observations.
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