Sentence examples for separability criterion from inspiring English sources

Two novel incremental feature weighting strategies are proposed in this paper, one based on a leave-one-feature-out, the other based on a feature-wise separability criterion.

Therefore, another classification criterion, the Fisher class separability criterion, is introduced into our methods.

In this study, we focus on the filter methods based on different evaluation measures, such as distance criterion (Relief and its variants ReliefF, IRelief [15]), separability criterion (Fisher Score [16]), correlation coefficient [17], consistency [18], and mutual information [11].

To solve this problem, the Fisher class separability criterion, as a supplement of to the traditional Bayesian framework, was adopted in our design for the extension of the PHD filter.

Also, we are employing a basic classification, as the objective of the experimentation is to validate the separability criterion (12), rather then assess a full 3D face recognition method, in the case of which, a more robust classification scheme shall be used.

We are concerned with establishing completeness and separability criteria for large classes of topological vector spaces which are typically non-locally convex, including Lebesgue-like spaces, Lorentz spaces, Orlicz spaces, mixed-normed spaces, tent spaces, and discrete Triebel Lizorkin and Besov spaces.

This upper bound yields to a class separability criteria that depends on (i) the within-class-scatter-matrix that shows the scatter of samples around the same class, and (ii) the between class scatter matrix.

A class separability measuring criterion C(k) is adopted in order to rank all the features.

Figure 7 Fusion with a separability measure.

This hypothesis is validated quantitatively using the separability measure.

As we have seen, given separability, locality and the Criterion of Reality both conditionals hold.