Sentence examples for the classifications will be from inspiring English sources

Initially the classifications will be monitored and regulated by BookRabbit, with the aim of reaching a 'self monitoring' Wiki model in due course.

By this algorithm, the "singular" samples will be eliminated according to the classification accuracy and the features that facilitate the classification will be enhanced.

Sometimes fairness will require a hearing to determine whether a statutory classification will advance the legislature's purposes in a particular case so that the classification can properly be used only as a burden-shifting device, while at other times the fact that a litigant falls within the classification will be enough to justify its application.

All items of the classification will be discussed and, whenever relevant and possi­ ble, be illustrated by MR images.

The neural networks take time in the training phase, and then, the classification will be done quickly.

Since Trainable Weka Segmentation processes each slice individually, the classification will be more accurate if the software can "see" artefacts (e.g. missing wedge streaking) and meaningful data in the same image so that it can identify differences between the two.

Future amendments of the classification will be based on scientific advances.

The classification will be based on a simplified version of the 'Ergonomic Abstracts' classification scheme [ 43, 44].

Fourth, Gracillarioidea, as defined by Davis and Robinson [ 11], clearly do not include Douglasiidae, and changes to the classification will be required.

When real coverage is neither high nor low, but moderate (i.e. between UT and LT), the classification will be less precise.

It suggests that there are no major grids in which the weight parameters can succeed in selecting near-native models in many entries, and therefore, the classification will be required.