To control for a potential confounding influence of illegally imported quantities of these drugs, the study incorporated UK Borders Agency data of Class A and Class C drugs seized while entering UK territory between 2006 and 2012.
Examination of these samples produces 'character data' often named 'descriptive data' (2, 3) and sometimes 'comparative data' (1)—a class of data referring to 'taxonomic characters' (4), which each have two or more states and can cover all data suitable to characterize a taxon in comparison with related or similar taxa.
In this paper, we derive a new generalized Volterra–Fredholm integral inequality and use it to study the dependence of solutions on the initial data for a class of fractional differential equations with Fredholm integral operators.
The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes.
In class-based randomization, related objects are included as data members of a class.
With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space.
Therefore, a collective review of these data as a class of agents may be most appropriate.
Both the Gaussian probability design and the fuzzy boundary design methods automatically search for regions, where training data are sparse, and add pairs of training data on both sides of a class boundary where the data density is the lowest.
The Gaussian Probability Design automatically searches for the sparseness of the data, and adds pairs of training data on both sides of a class boundary in areas where the data density is the lowest.
