Sentence examples for categorization approach from inspiring English sources

Essock and colleagues (Essock, Sinai, McCarley, Krebs, & DeFord, 1999; Essock et al., 2004) took a categorization approach to their tasks, asking participants to classify whether stimuli represented imagery types with content such as sky, trees, buildings, and water.

In [28] Schultz et al. proposed a hashtag-based categorization approach in order to test the applicability of several machine learning techniques in conjunction with relevant feature selection algorithms for the text categorization problem.

We demonstrate a bottom-up categorization approach combining the IBS method and hierarchical cluster analysis to detect subgroups of subjects with HRV phenotypes associated with β-AR polymorphisms.

In conclusion, this study shows that a bottom-up, categorization approach combining the IBS method and hierarchical cluster analysis can detect subgroups of subjects based on phenotypes that are associated with β2-AR gene polymorphisms.

Furthermore, a categorization approach assumes that the threshold values are intractable, when commentary has begun to suggest that the reference ranges for optimal benefit may be too low [27] and depend on the disease or population characteristics being studied.

We show that this bottom-up, categorization approach combining the IBS method and hierarchical cluster analysis can detect subgroups of subjects based on phenotypes that are associated with β-AR gene polymorphisms.

The aforementioned types of classifications cover a range of NR methods, but there is a need to broaden the categorization approaches in order to review currently existing methods in this area.

Different categorization approaches may have modified specific study findings and conclusions.

Further, there is variability in categorization approaches amongst studies that dichotomize into positive and negative groups.

Dictionary-based categorization approaches attempt to exploit lexical similarities between GO terms (descriptions and synonyms) and the input text to be categorized.

The potential ramifications of each source of uncertainty should be addressed and some crude classification or categorization approaches could be developed (e.g., low, intermediate, or high uncertainty) with respect to a given source.