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Due to the exponential growth of the model complexity with the number of satellites, new concepts must be investigated.
The main purpose of this study is to test the hypothesis that, with appropriate structures, increasing model complexity with the same model concepts would lead to an increase in the model efficiency in simulating either runoff or internal variables.
We compared three types of empirically fitted allometric models of varying model complexity with respect to the number of input parameters that are easy to measure at the ground: model type I based only on the diameter at breast height (DBH), type II which used DBH and tree height and model type III which used DBH, tree height and wood density as predictors.
Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality.
The Akaike information criterion (AIC) was applied for metabolite-specific stepwise model evaluation and reduction of model complexity with regards to fixed effects (all fixed effects except time were considered optional).
2) Model complexity with respect to the support of the model's coordinate polynomial functions.
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Going one step further, we combined the best performing domain-dependent criterion ERX (for α=0.4) with the normalized model complexity, to experiment with the combined ERXC criterion.
So the best trade-off between accuracy and model complexity is found with 20 Gaussian processes with a vocabulary size of 10.
Performance is examined for various levels of model complexity and compared with that of statistical IRT-based techniques.
Note that the time required for model development and the model complexity increase along with higher cost and lower epsilon value.
The main drawback of that strategy is the model complexity, which grows with the application size, hence causing considerable negative impact on simulation time.
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