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Recently, Ziari and Raissi (2016) proposed an approach to rank the efficient DMUs in DEA based on minimizing distance of the under evaluation DMU to the frontier of efficiency.
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Although majority-rule supertrees and RF supertrees are both based on minimizing RF distance, they represent two different approaches to supertree construction.
The separation criteria is defined based on minimizing the distances of samples from the same class while maximizing the distances of samples from different classes.
Semi-landmarks are slid along the curve until they are in positions that most closely match the reference configuration [ 32, 33] based on minimizing the Procrustes distance [ 34- 36].
They proposed a camera calibration method for estimating the focal length of the camera and the distance from the camera center to the interface based on minimizing the length in the world space of a known straight object.
This analysis was based on minimizing the square distances between corresponding points and scaling all tracings to a common size.
Like the Procrustes approach, this method is based on minimizing the sum of squared distances between corresponding points.
For general distances, mapping functions can be derived by two different approaches the first approach is Multidimensional Scaling (MDS) in which an optimal mapping is derived based on minimizing the differences between the two distances over a finite dataset [44].
Standard fuzzy clustering models like fuzzy c-means are based on minimizing the total cluster variation, which is defined as the sum of the distances between the data points and their corresponding cluster centers weighted by the membership degrees.
Earlier approaches [1 3] were based on minimizing the maximum error.
Chemical equilibrium model is based on minimizing Gibbs free energy.
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