Sentence examples for categorisation model from inspiring English sources

The overuse definitions are consistent with the approach advocated by Clarsen et al. 19 It is recognised that for some real-world datasets some of the dependency categories within the SIC model may not apply (with the potential for some zero count cells), but it is still important to include them in an overall categorisation model, especially from a statistical model point of view.

The strengths of the investigation include the application of two commonly applied MMSE categorisation models along with the MRC CFAS groupings to a large population-based sample of older persons.

After imposing the disease/health problem categorisation, the model hypothesises that the disease burden can potentially be addressed by intervening through: i) primary prevention targeted at persons at risk, ii) early diagnosis (and treatment) of persons with undiagnosed diseases, iii) management of persons with established disease and iv) palliation for persons in the terminal phase.

The latter exercise enables categorisation of models with respect to their performance if more than one model is available, or developed, for any given task.

We therefore summarized the processes behind this categorisation in a model and conducted a study in a laboratory setting during which subjects were asked to rate a variety of rural road pictures.

The results have implications for the assessment of environmental effects through appropriate street categorisation in emission models, as well as the possible reduction of environmental effects through better traffic planning and management, driver education and car design.

Further information on food categorisation for dietary modelling is provided in Additional file 1.

In this paper, we use the hydrophobic-polar categorisation of the HP model within a hydrophobic-core directed macromutation operator; however, most of the time the search is guided by the BM energy model.

One of the problems with local models is finding enough compounds that fall into the similarity classes to make statistically robust models, though categorisation can be useful in predicting sensitisation by read across.

We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome.

We establish the SLICER conceptual framework built on multilevel modelling principles and the differentiation of basic semantic relations (such as specialisation, instantiation, specification and categorisation) that dynamically structure the model.