Sentence examples for features in the classification of from inspiring English sources

The other part relates to the point feature derivation using a novel 3D local feature descriptor LSSHOT, designed for extracting features in the classification of points.

To summarise this experiment, we have produced evidence to suggest the inclusion of typed dependencies as features in the classification of cyber hate reduced false positive rate in the classification of 2 out of 3 types of hate speech - race and sexual orientation - when compared to using a BOW model and/or hateful terms as features.

The TP-loadings in Table 2 are able to rank the influence of the different gait features in the classification of elderly fallers and nonfallers.

A consensus on a procedure to compare the abilities of different gait features in the classification of elderly fallers and nonfallers, like PLS-DA, might have important value for evaluation of new features of gait stability and variability.

We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients.

Although not conclusive, our results seem to suggest that the combined use of DCE-MRI and DW-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions.

By using the same method, one finds that, in order of prominence, the polarity, hydrophobicity, amino acid composition, and solvent accessibility play more prominent roles than other feature properties in the classification of lipid-binding protein (Lin et al. 2006c).

The potential of cortical thickness for the classification of AD has already been proposed [ 47– 49], and thus it is of no surprise that such features could aid in the classification of HC and MCI subjects.

Their aim was to register pairs of images, from light microscopy and infrared spectroscopy, in order to extract morphological features for use in the classification of cancer versus non-cancer cases.

The choice of a subset of EEG channels is one of the selection features for the classification of EEG signals in BCI systems in order to avoid dealing with high dimensional and noisy data.

In addition, we use motion features for the classification of video episodes, which means key frames should reveal a change in motion information as well.