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Because it is resources composed of PC clusters that meet the level of high-computing power, it consumes minimal computing cost.
Nowadays, the development of powerful computers makes the reduction of computing cost less relevant for pedigree-based analyses but the reduced model can also be used to exploit marker-based relationships.
Simulating particle coagulation using Monte Carlo methods is in general a challenging computational task due to its numerical complexity and the computing cost.
The computing cost was less than $4,900 a hour.
For the third load case, the computing cost with Tabu search was higher by 302%.
This approach converges fast and can potentially save on computing cost.
Therefore, CSA preserves necessary information for classifiers with very low computing cost.
Thus, the computing cost is significantly reduced compared to other sensitivity analysis methods.
Feature selection approach is designed to improve the classification accuracy and reduce the computing cost.
However, existing SA techniques are not designed for text categorization and often incur huge computing cost.
Influential factors are also introduced to balance the model accuracy and computing cost.
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