Sentence examples for many classifications in from inspiring English sources

As with many classifications in musculoskeletal imaging, multiple versions of the same classification system were used, mixed up, or inconsistently used.

The geological aspect of the study region shows that there are many classifications in it such as Ultrabasics-peridotite, mixture of Ultrabasic and Gabbroic rocks, Gabbros and Ultrabasics etc.

RFs have been receiving increased attention in the data-mining field as a means of variable selection in many classification tasks in computational biology, including the selection of a subset of genetic markers and genes in microarray data analysis relevant for the prediction of a certain disease [ 47- 50].

The detrimental effects of confounders are observable in many classification tasks in molecular biology, as illustrated by the following two examples: one may want to predict the phenotypes of plants based on their genotype, typically represented by single nucleotide polymorphisms that represent sequence variation in an individual.

Many classifications are not in line with expectations from experience, such as severe classifications of substances derived from some basic food plants or lacking classification of known medical plants or plants with sensitizing potential.

For the purpose of data collection from verbal autopsies, categories such as "fetal," "cord," and "placenta," typically found in many classifications, could easily be combined, e.g., into "other intrauterine," as exemplified in Figure 5.

There are many classification tasks, in particular the type of text classification that we encountered in Chapter 13, where classes can be distinguished by word patterns.

In many classifications tasks the test data should have the same distribution of outcome variables.

There are many classification problems in petroleum reservoir characterisation, an example being the recognition of lithofacies from well log data.

There appear many classification methods in the literature.

Random forest (RF) is a non-parametric classification algorithm that uses many classification trees in parallel (Breiman, 2001).