Sentence examples for diverse set of features from inspiring English sources

It is very difficult to find other large-scale, publicly accessible datasets with a similarly wide and diverse set of features.

Manually we might never be able to produce best and diverse set of features that closely describe all the variations that occur in our data.

This paper makes use of selected case examples to describe the richly diverse set of features, ranging from paleontology to sedimentology, to structural geology, which are associated with the field identification of basin-margin unconformities.

With this in mind, a diverse set of features extracted from SAAC, PseAAC, and PSSM are adopted in this study to encode protein sequences.

Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features.

In addition, using a diverse set of features also allows us to investigate the effect of the different interpolation methods on the resulting classification rates.

The primary visual cortex (V1) encodes a diverse set of visual features, including orientation, ocular dominance (OD), and spatial frequency (SF), whose joint organization must be precisely structured to optimize coverage within the retinotopic map.

In both function and content, thematic maps are fundamentally different from navigation maps, which serve the way-finding needs of drivers, pilots, and tourists, and general-purpose or reference maps, which portray a diverse set of basic features such as coastlines, terrain, and transport routes.

One reason for the outward pointing side chains might be caused by structural differences between the GPCRs, because precisely these helices are characterized by a very diverse set of helix features [75] such as bulges and proline induced or supported kinks.

Developing a streaming service to run on a diverse set of feeble feature phones requires serious engineering talent; 70% of Saavn’s costs are labor.

Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations.