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Typical multiscale methods have two essential components: a macro and a micro model.
Traditional feature selection methods have two major inappropriate designs in their criterion.
These methods have two desirable characteristics, namely, robust numerical convergence and efficient parallelizability.
Stereo matching based obstacle detection methods have two inherent limitations, including sensitivity to illumination variation and high computational complexity.
However, ensemble-based methods have two key assumptions: Sufficient number of ensemble members is needed and the mean of the ensemble should be the best estimate among the realizations.
The main advantage of this method comparing with the present interpolation methods have two points: (1) the interpolation function is carried out by a simple and explicit mathematical representation through the parameter α; (2) the shape of the interpolating surface can be modified by using the parameter for the unchanged interpolating data.
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These published methods have three major limitations.
However, in our opinion, their methods had two major limitations.
This is mainly because ensemble-based optimization methods had two distinct advantages.
For daughter pregnancy rate, the four methods had two common SNP effects on the X chromosome.
The method has two different uses.
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