Sentence examples for classification steps to from inspiring English sources

We combined a clinical pathway analysis and the subsequent categorization and classification steps to obtain grouped pathway diagrams for cancer diagnostics and treatment.

Since buildings are typically controlled in different modes depending on the time of day (night setback), an identification of a switched system is proposed, combining a classification step to identify the switching times followed by the identification of a piecewise linear model for each mode.

The clusters derived from three different dissimilarity matrices were used in the Bayesian integration and classification step to obtain CA scores for each combination of parameters.

Averaged images obtained from the classification steps are referred to as class averages; those repeats that form the class are referred to as class members.

Obviously, the clear sample separation information conveyed by the self-clustering mechanism of the meta-samples is almost impossible to obtain from the original high-dimensional data directly, and the key discriminative features captured by our proposed MICA method would be able to facilitate the subsequent classification step and contribute to high-accuracy disease diagnosis.

The goal of this section is twofold: first, to highlight the benefits introduced by the classification step with respect to a class-unaware attack; while performing such task, we also demonstrate the effectiveness of the attack procedure against various SIFT software; second, to address a specific forensic scenario by countering the copy-move detection technique described in [9].

Geoprocessing Web services are used as functionality blocks to finish basic classification steps.

These numbering and classification steps were further aided by reference to Dr. Andrew C.R. Martin's Group website (http://www.bioinf.org.uk/abs/chothia.html).html

This classification step is specular to the one described above for the price.

First, a coarse feature classification step is performed to detect the cavity candidates by capturing the geometric, textural, and gradient features in the lung field.

Furthermore, we showed that tools not relying upon a classification step may lead to spurious biological interpretations.