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The first and the second methods were based on the Radial Basis Function (R.B.F) neural networks.
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The first method is based on linear matrix inequalities (LMIs) and the second method is based on model reduction.
And the second method is graph partitioning base on sparse cut.
One method is for the two-player game and the second method is for the n-player game.
The first method used the mixed dye solution of five standard dyes and the second method used the separate dye solutions for each standard dye.
The first method considers a chemical correction of the initial activity (14A), and the second method involves chemical and isotope issues.
The first method is to obtain a difference spectrum before and after selectively etching the Si cap and the second method involves peak-fitting and deconvolution by software.
On the basis of confidence regions, two different methods for obtaining soft information are proposed: The first method is based on a linearization of the nonlinear parameter estimation problem and the second method is based on the likelihood concept.
Most scientists have followed the first approach so far, and the second method has emerged as a versatile approach for combinatorial biomaterials discovery.
The first method is the typical SMVAD proposed by Sohn et al. [2] as described in Section 2, and the second method is SMVAD with the HMM hangover scheme which is specified in [3].
The first method is linear over the concentration range of 5 to 45 μg/ml, and the second method is linear over the range of 50 to 450 μg/ml.
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