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In [2], Kim et al. used a convex metric method that maps the received signal to noise ratio (SNR) to a Gaussian capacity.
It is a variable metric method that attempts to find an optimal step size in parameter space and is related to Newton's method in the sense of following gradients toward a solution but enhanced in the region of a minimum by a quadratic approach.
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We propose a supervised distance metric learning method that exploits the structure of a known reference taxonomy to guide the learning process (see Materials and Methods).
We have developed a non-metric calibration method that computes the radial and tangential distortion parameters of an imaging system without the use of any calibration objects.
Second, developing a new multi-metric election method that can be used by network nodes to determine their suitability to become cluster heads.
ClusterSim identifies the combination of normalization method, distance metric and classification method that produces the highest cluster validity index score considering 2 to 50 possible clusters (http://cran.r-project.org/web/packages/clusterSim/clusterSim.pdf).org/web/packages/clusterSim/clusterSim.pdf
A combination of an Image Quality Metric (IQM) and an accurate saliency metric is used to select a fusion method that is optimal for semi-hidden target recognition.
The science of achieving the Third Metric is not an abstract idea, but a method that can be learned by anyone willing to learn a few simple skills -- skills that help you design your life for true success.
We have used a statistical method that takes account of the non-metric properties of ordered categorical data.
Surprisingly, OMA, the OD method that incorporates phylogenetic tree information, exhibits the worst performance according to this tree-based metric.
MLR is a method that adopts rough templates and performs prediction based on the weighted RMSD, where the weights are determined using the metric learning algorithm.
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