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Because R 2 tends to increase with model complexity, the Akaike information criterion (AIC, Akaike 1974), which penalises complex models to determine a balance between model fit and model simplicity, was also used for model selection.
Due to the model complexity, the analysis and design on this model is difficult to perform.
For a given model complexity, the multilayer perceptron (MLP) neural network outperformed the other approaches in terms of accuracy.
Model complexity The MIP model developed belongs to the class of knapsack problems, which are known to be NP-hard (cf. Kellerer et al. 2004; Pisinger 2005).
However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements.
As shown on the simulated data, the benefit of multi-task learning depends on the model complexity, the number of training instances of a task, and the availability of a similar target.
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The best model according to MDL is the one that minimizes the sum of the model complexity and the number of bits required to encode their errors.
Taking into account the increased model complexity of the random effects approach, the DIC with the random effects model is also lower.
Due to the exponential growth of the model complexity with the number of satellites, new concepts must be investigated.
The implementation of natural or synthetical scaffolds elevated the model complexity to the level of three-dimensional cell culture.
Since the probabilistic model of MixClone is a generative mixture model, the model complexity and the corresponding log-likelihood increases as K increases.
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