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To realize a fixed set of features in a fixed amount of time with a given set of resources is only possible if the time is sufficient to realize all these features.
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Design optimization scenarios with predefined and fixed sets of performance requirements are presented in literature.
In traditional RCs, the numbers of reconstruction sets and regeneration sets are fixed to some specific values.
Feature-based machine learning algorithms require the instances to have a fixed set of attribute values from a feature space.
All of the existing work involves finding the solution for a given fixed set of parameters.
that represents the two sets of features.
This method evaluates whole sets of features.
Good chance of fixing.
Most of machine learning algorithm requires fixed length of input features.
Let FixedSubset be the set of features to be fixed in the final result and let c be the class number.
Once the set of features used for gene finding is fixed, the CRF model can be trained.
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