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One alternative method is interpolation, and a Lagrange interpolating polynomial method is presented below [40].
The method is presented below to show how reliable research data was generated for forecasting calculations in order to simulate the situation in 2015: 1.
Algorithms were computed only on the cortical rim, obtained with a masking process (described in § 2.2.2) A brief description of each method is presented below: 1 The coefficient of variation (CV) is defined as the ratio of the standard deviation and the mean of the intensity levels.
This method is presented below: Theorem 2 If A is a G C O ( m, n, ℐ, S ) matrix and P is a w × w invertible matrix with entries belonging to the set ℛ, then P ⊗ A is a G C O ( m w, n w, ℐ, S ⋅ ℛ ) matrix where S. ℛ is a set of all products of the elements of S and ℛ.
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The training and conversion stages of the proposed GB conversion method are presented below in Sections 3.1 and 3.2, respectively.
The main steps of the proposed method are presented below: 1- Apply feature-level and score-level fusion using HOG and LBP in addition to decision-level fusion using PCA, HOG, and LBP. 2- Partial decisions from each level of fusion will be acquired as follows: If (Partial Decision=Genuine), then Ri=1, else (Partial Decision=Impostor) Ri=0.
Complete descriptions of the specific source-apportionment methods have been published elsewhere (Kim et al. 2004, 2003a, 2003b; Marmur et al. 2005), but brief summaries for each method are presented below.
A brief description of these methods is presented below.
The main findings regarding the suitability of tested evaluation methods are presented below.
Detailed methods are presented below.
Detailed data collection methods are presented below.
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