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This data pruning reduced the database size or the model size by about 30%, and consequently saved the computation time and memory usage in speech recognition.

However, it is also often desired to reduce the model size by excluding inconsequential chemical species and/or reactions for end-point applications, usually due to computational reasons.

This LRT's null model is formed by imposing a single, non-boundary constraint on the alternative model (i.e., the constraint that ω3 = ω4), reducing model size by one estimated parameter.

θ s (equation 3) penalises the model size by summing across the number of inputs (v e ) of each edge selected in model P and dividing by the total number of inputs across all edges (v e s = ∑ e = 1 r v e ).

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Figure 2B shows the development of the regression error for reduced model sizes by stepwise elimination of positions.

Furthermore, we evaluated reduced model sizes by stepwise elimination of context positions carrying no or little information for RTP prediction (feature selection).

This first step allows to reduce the acoustic-model size by a factor of 60.

The dependence of model uncertainty on the model size is also investigated by exploring skeletal models containing different number of species.

Existing solutions are significantly limited by model size.

While mean loads are not significantly affected by the model size, peak loads are sensitive to both the geometric scale and the spectral content of the test flow.

The effect of model sizes was investigated by comparing the adsorption energies of different models with CO2 located on the same adsorption sites.

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