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In this section, we propose a greedy algorithm for the CNDP.
Third, we propose a Greedy algorithm for the construction of an approximation space/procedure valid over a range of parameters; the Greedy is informed by the a posteriori error indicator developed in this paper.
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We have thus proposed a greedy algorithm for estimating optimal subsets and demonstrated that the resulting classifier can produce more accurate classifiers in both simulated and real applications.
Levy, 2007 [ 6] proposed a greedy algorithm for the haplotype assembly of a diploid individual genome.
In case of spectral pairs, Savitski et al. (2005) proposed a greedy algorithm (for CID/ECD spectral pairs) that significantly boosts the performance of de novo sequencing.
Because of the NP-hardness, we then propose a greedy algorithm to achieve approximate solution for problem Eq. (3), which involves two steps.
Thus, we propose a greedy algorithm to find an approximate solution for the chaining problem.
To speed up this process, we propose a greedy algorithm, called GreedySelect (GS).
Next, we propose a greedy algorithm to determine the optimal design of review strategies.
Subsequently, we propose a greedy algorithm that is shown to be a constant-factor approximation to the optimal deployment.
To address the limitations of exisiting algorithms we propose FlexSnap, a greedy algorithm for flexible sequential and non-sequential protein structural alignment (the name of the algorithm is a non-sequential permutation of the bold letters in Flexible non- Sequential protein alignment).
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