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This initialization process is used in this paper to test the method and produce the results in Section "Results".
Sparse representation based classification (SRC) has been shown to be an effective method and produce impressive performance on face recognition.
Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results.
We utilized a completely digital simulation such that the surgical procedure and the prosthesis design could be freely adjusted during simulation to decide the best treatment method and produce an individualized prosthesis according to the design.
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This is also called the "DEM reconditioning", which uses the "AGREE" method and produces a "hydrologically corrected" DEM (Zhang et al. 2013).
NL implemented the reconstruction method and produced the model.
This approach is referred to as the 'unsupervised' method and produces 'fine' functional families (FineFams) [20].
We created five imputed datasets using the Markov Chain Monte Carlo method and produced a monotone missing data pattern in this large dataset.
Phylopythia is based on a support vector machine (SVM) classification method and produces very good accuracy for long (≥ 1 Kbp) reads (McHardy et al., 2007).
Amphotropic retroviral packaging cells, φ NX-ampho, were transfected with 10 μg of cDNA library using CaCl2 method and produced up to 2 × 10 infectious units ml−1.
Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM) methods, and produce highly interpretable models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com