Sentence examples for model complexity was from inspiring English sources

The model complexity was reduced by ignoring the spatial variations and assuming isothermal stack operation.

With this procedure, even better performance than with high-dimensional input was obtained (2 3 % WER reduction), and also the model complexity was considerably reduced.

However, based on the estimated parameters of the linear model of the ANOVA on the proportion of 'high-memory' (goodness of fit = Radj2 = .35) a difference in distance condition depending on model complexity was apparent, indicating an ordinal interaction.

For all individual and fused models the optimum model complexity was determined to be 1LV.

The level of confidence for each level of model complexity was tested by analysis of variance of the related decrease in residual variation.

This lower penalty for model complexity was reflected by a lower effective number of parameters (5152 for the SEM and 6829 for the multi-trait model).

Together with the performance figures of interest, methodological issues concerning the choice of the most appropriate model abstraction level, the adoption of a compositional modelling approach, and the management of the model complexity are also discussed.

Three techniques in increasing order of model complexity are discussed.

The model complexity is increased stepwise by adding components to an existing 2D overland flow model.

Thus, this optimal model complexity is dependent on the size of the data set.

However, the increase in model complexity is not always justified with statistically significant increases in predictive accuracy [107, 108].