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The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1].
The approach TPPB generates candidate blocks satisfying k-anonymity and uses public reference values as the RVs of blocks.
Riminder generates candidate rankings for open jobs by comparing applicant resumes against resumes from current employees and others in the world with similar job titles.
VMSP automatically generates candidate templates of sequential patterns, and then identify valuable patterns as design operations based on the interestingness we set.
The LAMP algorithm generates candidate formulas that are passed to a Markov Logic Network (MLN) for selecting the most likely subsets of candidate formulas.
Initially, the TALA generates "candidate" target locations, and then performs "soft" nearest neighbour data association (e.g. [21]), allowing each measurement to be associated with more than one candidate location.
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The approach in [4] uses forming generalized hierarchies to generate k-anonymous blocks, which may make the RVs over-generalization and reduces the accuracy of generating candidate pairs.
Time complexity to generate candidate set = ((I+1 C^{f}_{I}).
During the design process, the generated candidate solutions are evaluated.
The number of generated candidate patterns has a significant effect on runtime performance of algorithms.
Therefore, the number of generated candidate patterns also decreases as the database size becomes larger.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com