Sentence examples for multiple classifications using from inspiring English sources

Thus, strategies for using TRIBE-MCL range from simply using program defaults [22] or arbitrary user-defined values [23] to the generation of multiple classifications using different parameter values [24] and the use of parameter values found to be globally optimal with respect to some empirical quality measure [25].

Because of the different ways to classify exposures and the multiple classifications used (even for job titles or industries, each study using their respective national classification), results are difficult to compare.

This paper proposes multiple harmonic-source classification using a Self-Organization Feature Map (SOFM) network with voltage (V)–current (I) wavelet transformation patterns.

To identify the "race" attribute in a face image, a multiple classification is used under the "one versus all" scheme, where the objects of the "Caucasian" class are identified first based on the proposed modification of the binary classification, and then the remaining objects are classified as either "Asian" or "Black".

The approach was focused on multiple object location and classification using fractal geometry to evaluate the texture as a set of features from an image which was already segmented using gradient operator and a novel region growing method.

In order to evaluate the performance of the proposed solution to the problem of feature selection for graph classification using multiple side views, we tested our algorithm on brain network datasets derived from neuroimaging, as introduced in Sect.

This study presents an approach for single and multiple power quality disturbance detection and classification using multidimensional analysis, higher-order statistics and a neuro-tree based classifier.

Models designed through multiple memberships multiple classification, 34 or using a mixture of Polya trees, 35 or multivariate CAR 23 offer desirable properties.

In addition, perhaps Intrusion Detection may need to use more than binary classification in some scenarios and use multiple classifications (i.e., "attack", "normal", "suspicious", "unknown", etc).

The multiple classifications systems in use are often complex and are primarily focused on high-income countries.

And this is probably an underestimate--many institutions use multiple classifications for postdocs and have no employment-tracking system, making it very difficult to get an accurate count.