Add to ORKGWe discuss a number of distance functions encountered in the theory of computation, includin
In this paper, we provide an application-oriented characterization of a class of distance functions ...
A distance on a set is a comparative function. The smaller the distance between two elements of that...
Based on the analysis of conditions for a good distance function we found four rules that should be ...
We apply the techniques of computable model theory to the distance function of a graph. This task le...
Suppose $H$ is a space of functions on $X$. If $H$ is a Hilbert space with reproducing kernel then t...
In analysis, a distance function (also called a metric) on a set of points S is a function d:SxS->R ...
peer reviewedThe concept of n-distance was recently introduced to generalize the classical definitio...
AbstractIn image processing, the distance transform (DT), in which each object grid point is assigne...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
This 4th edition of the leading reference volume on distance metrics is characterized by updated and...
In this paper, digital distance functions using sequences of weights are studied and used to approxi...
Laplace, Georges [1975], “Distance du Khi 2 et algorithme de classification hiérarchique. Formule du...
Many pattern recognition and machine learning approaches employ a distance metric on patterns, or a ...
A generalization of Shephard's distance functions is proposed, extending the usefulness of distance ...
We pursue the investigation of the concept of n-distance, an n-variable version of the classical con...
In this paper, we provide an application-oriented characterization of a class of distance functions ...
A distance on a set is a comparative function. The smaller the distance between two elements of that...
Based on the analysis of conditions for a good distance function we found four rules that should be ...
We apply the techniques of computable model theory to the distance function of a graph. This task le...
Suppose $H$ is a space of functions on $X$. If $H$ is a Hilbert space with reproducing kernel then t...
In analysis, a distance function (also called a metric) on a set of points S is a function d:SxS->R ...
peer reviewedThe concept of n-distance was recently introduced to generalize the classical definitio...
AbstractIn image processing, the distance transform (DT), in which each object grid point is assigne...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
This 4th edition of the leading reference volume on distance metrics is characterized by updated and...
In this paper, digital distance functions using sequences of weights are studied and used to approxi...
Laplace, Georges [1975], “Distance du Khi 2 et algorithme de classification hiérarchique. Formule du...
Many pattern recognition and machine learning approaches employ a distance metric on patterns, or a ...
A generalization of Shephard's distance functions is proposed, extending the usefulness of distance ...
We pursue the investigation of the concept of n-distance, an n-variable version of the classical con...
In this paper, we provide an application-oriented characterization of a class of distance functions ...
A distance on a set is a comparative function. The smaller the distance between two elements of that...
Based on the analysis of conditions for a good distance function we found four rules that should be ...