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Opposed to this full approach, the approximation of the partial low-pass filtered scattering potential F in only one direction of k⃗0 gives rise to optical sectioning, as discussed in section 3.3.
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We then propose a hybrid approach employing the approximation algorithm in combination with {0,1} LP.
This leads to a regularization approach to the approximation problem and can be easily implemented by the radial basis neural network.
Data envelopment analysis (DEA) as a nonparametric approach permits the approximation of the efficient boundary of technologies.
In order to avoid dense graphical figures, we display the comparison of two approximations, that is, our approach and the approximation reported in [11, ], in separated examples.
In these three figures, we plot the relative errors deduced from our approach and the approximation reported in [11] in the log-log scale.
However, we can consider a simulation based approach to the approximation of CES given in Equation (2).
As indicated above, the main disadvantage of the pollutant-based approach is the approximation or uncertainty in using relationships in PM2.5 from outdoor air to quantify the health effects of PM2.5 from indoor air sources.
As the number of permutations sampled approaches infinity, the approximation of that unknown distribution approaches the actual distribution.
Using this approach, the final approximation equations do not impose any limitation on the element shape.
In the present approach, the linear approximation and with it the cochlear filter depend on the parameter c.
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