This method guarantees the sum of the near-field & far-field converge pointwise to results satisfying periodicity and compatibility conditions.
The mortality index was expressed as a percentage and calculated as the number of maternal deaths resulting from a particular obstetric condition divided by the sum of the near-miss morbidities and maternal deaths resulting from such obstetric conditions.
(9) WOD a ij = ∑ l = 1 k w jj ' d ij ' j | j ' = o ijl ∑ l = 1 k w jj ' | j ' = o ijl By contrast, the WODb approach first applies the scaled weights, computes the nearest absolute expression differences and then finds the sum of the k nearest weighted differences.
A channel estimate at an arbitrary data position using a linear interpolation is given by a weighted sum of the three nearest channel estimates (in the Euclidean sense of the time-frequency grid) at pilot positions, that span a plane.
Thus in order to uncover the randomly organized architecture of BTNs, new measurements of the average sum of the nearest-neighbors' degree degree correlation Dnn k), and the degree average edges among the nearest-neighbors L(k) are proposed.
The prediction for this classifier is done through a weighted sum of the k-nearest neighbors of the source instances deemed most similar.
The second sum is over the nearest neighbors of i site.
It is discussed in this paper that the linear form of weighted sum in the nearest-neighbor matching function may not well address the nonlinear effects of attributes toward the overall similarity, and the imprecise nature of weights associated with attributes may fail to produce robust results in care retrieval.
Using the orientation histogram, we can calculate the eight direction distributions in the ranges of (0,π,5π/4,3π/2 ,7π/4/4,3π/2,7π/4) with the length corresponding to the sum of the gradient magnitudes near that direction within the region.
As the giant that has won 21 Bundesliga titles, more than the sum of its nearest five opponents, Munich has to respond.
The imputed value (widehat{y}_{i}) is expressed as a weighted sum of the responses taken from the nearest k reference observations as follows: widehat{y}_{i} = mathop sum limits_{j = 1}^{k} w_{ij} y_{j}^{i} (6 where y ij i, j = 1, 2,…k is the set of the response variable observations for the k reference set elements that are nearest to the ith target set elements in the feature space.
