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This algorithm is developed to solve multi-objective optimization problems by evaluating a set of single objective sub-problems.
We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data.
Biologists have classically addressed this problem by evaluating a set of random Monte Carlo simulations with parameters between an operation range.
We illustrate the applicability of our method to personalized recommendation algorithms by evaluating a set of sample parameters and data sets and find that for the parameters, we choose strong personalization actually leads to poorer discoverability and navigability.
This is achieved by evaluating a set of positive and negative controls by receiver operating characteristic (ROC) analysis.
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The principal component analysis (PCA) compresses and classifies data by evaluating a new set of variables, smaller than the original set of variables, which holds most information of the dataset.
28, 32 GA is advantageous in that during the exploration of the space of possible solutions, it does not evaluate solutions one by one but evaluates a set of solutions simultaneously.
The main drawback of such procedures is the added computational cost associated with the need to evaluate a set of designs by performing multiple finite element analyses.
Why analyze the data by evaluating a very dense set of markers (>238,000) when perhaps only 50∼20,000 are truly independent observations?
Statistical analysis and average size distribution of the particles were determined by evaluating a minimum of 100 nanoparticles per sample set of Gd:Fe3O4, Au Ag = 1 1, and Au Ag = 5 1 nanoparticles.
The system is evaluated by a set of 55 questions posed by NASA engineers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com