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For convenience, we use the greedy algorithm to select the support samples.
Solving the problem from the perspective of an approximate algorithm, the simple idea is to use the greedy algorithm.
In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem.
To check the source of seeds, we randomly generate 50 mixtures of two topics for both Arnetminer and Flixster, and use the greedy algorithm to select seeds for the mixture and the constituent topics.
Here we use the greedy algorithm discussed in [ 43] and freely available http://www.lambiotte.be/codes.html, which performs well against various benchmark tests [ 44].
In these cases, it would be computationally infeasible to use the greedy algorithm to select partitioning schemes, and where possible the relaxed hierarchical clustering algorithm should be used instead.
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The segmentation process is performed by solving an optimization task using the greedy algorithm approach [5].
Summing Oner, we obtain that (12).
The initial population of 101 spike trains includes the central spike train calculated using the greedy algorithm.
They selected random projections as the feature extraction method and solved sparse representation using the greedy algorithm.
Similar to our work, they took into consideration the edge structure; in addition, they used the greedy algorithm for modularity optimization.
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