Sentence examples for great computational cost from inspiring English sources

This kind of model is very simple to implement, since it does not require expert knowledge of the MFC internal processes or great computational cost.

Especially for nonlinear problems, the implicit time integration scheme will bring about great computational cost due to large degrees of freedom of the local DGM and the need to solve nonlinear equations.

The resulting stiff system of partial differential equations for ice velocity is either solved implicitly at great computational cost, or explicitly with added pseudo-elasticity (elastic viscous plastic, EVP).

We have also shown that increasing the interleave number for SMURFLite can have dramatic effects on performance, but at a great computational cost.

The online server sets the interleave threshold (the parameter that determines the complexity of the MRF) to 2; we have also shown that increasing the interleave number for SMURFLite can dramatically improve performance, but at a great computational cost.

To model spatial spread most completely would require following each cell in the genital tract with an agent-based model as well as accurate spatial elaboration of the genital anatomy (Bauer et al., 2009): this would entail great computational cost and inclusion of many unknown parameters.

These problems are interrelated, since a greater quantity of information and detail involves greater computational cost.

The simple Monte Carlo method requires a large sample size resulting in greater computational cost.

This approach to filtering can be used a second time (although at greater computational cost) for feature similarity by using the individual feature counts.

This improvement in detection accuracy comes at the expense of much greater computational cost, however (see Section 5 for detection accuracy and performance results).

Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average.