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The proposed method is for ranking extreme efficient DMUs in DEA based on exploiting the leave-one out and minimizing distance between DMU under evaluation and virtual DMU.
In the present paper, we proposed a model for ranking extreme efficient DMUs in DEA by exploiting the leave-one out and minimizing distance between DMU under evaluation and virtual DMU.
Mapping of each user and item to latent features is considered as an optimization problem of minimizing distance between R and (Q^TP) that is usually solved using stochastic gradient descent (SGD) or alternating least squares (ALS) methods.
The choice of α was empirically chosen and indicates a slight preference towards minimizing distance changes over appearance changes during the extension phase.
In order to gain the ranking score of DMUk by exploiting the new efficiency frontier, we suggest a new model by by using the leave-one out idea and minimizing distance between DMU under evaluation and virtual DMU.
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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Good edge location: minimizes distance between the detected edge and the actual edge.
This result indicates that while the monkeys travel in zones of low resistance, they do not minimize distance or landscape resistance between travel nodes.
In an attempt to minimize distance, we removed 11 C-terminal aa from CFP, as well as 5 more aa from the linker (CFPΔ; Figure 1).
By manipulating scale, location and orientation, Procrustes aims to produce an optimal superimposition as defined by some criteria that acts to minimize distance between corresponding points.
Processes were standardized and the physical layouts adapted to minimize distances.
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CEO of Professional Science Editing for Scientists @ prosciediting.com