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The model uses as basic components anytime algorithms whose quality of results improves gradually as computation time increases.
Instead, rewards are based on relative improvement, judged after the event so the quality of results can be taken into account.
The trade-off between computational efficiency and quality of results is primary focus of this research.
The correlation quality of results obtained from both methods is 98.8%.
Experimental evaluations show the high quality of results and their suitability for biomedical investigations.
The influence of network training parameters on the quality of results is also discussed.
We show that while our techniques maintain the same quality of results as currently used techniques, they use up to 5 times fewer resources.
Here, we show how non-cooperation causes unacceptable degradation in quality of results, and present an economic protocol to address this problem.
We show that while our techniques maintain the same quality of results as currently used techniques, our techniques use up to 5 times fewer resources.
However, the computation power increases significantly and a compromise between efficiency and quality of results has to be found.
Such studies need a careful planning to obtain the desired quality of results with the available resources.
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