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Earth mover's distance is a method to evaluate distance between two multi-dimensional distributions by linear programming [44].
In goal programming approach, we use 1-norm distance (city-block distance) to evaluate distance between objective function and ideal point.
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We use Ward's method [40] (uses an ANOVA approach to evaluate distances between clusters attempting to minimize at each step the sum of squares of any two (hypothetical) clusters that can be formed at each step) on our data set (normalized) to perform cluster analysis.
Standard methods such as K-means and hierarchical clustering evaluate distances between data points using all (equally weighted) features.
MDS enables one to easily visualize and evaluate distances between two objects considering at the same time the influence of all other objects.
The last value produces an approximation of the joint distribution of two independent normal variates, whereas the setting with μ = 1 evaluates distance between a 'bivariate Cauchy' distribution and the distribution of two identically and independently distributed Cauchy random variables.
The main division among variants is between MR and MR. The first evaluates distances between the pruned supertree and each input tree, while the second evaluates distances between the supertree and extended input trees.
In order to evaluate the distance between neighbors' historical velocity and node A's historical velocity, we introduce degree of dispersion.
The energy transfer in biochemistry is important, because the efficiency of transfer can be used to evaluate the distance between the ligand and the chromophore in protein.
Based on our discussion of NSS, we seek to evaluate the distance between the gradient statistics of an (ostensibly distorted) image and a statistical model of natural scenes.
In this paper, a four-angle-star based visualized feature generation approach, FASVFG, is proposed to evaluate the distance between samples in a 5-class classification problem.
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