Sentence examples for subtle classifications from inspiring English sources

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It was the Stoics who systematically developed a logic of propositions and devised subtle classifications of predicates.

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In addition VAA users tend to avoid taking 'extreme' positions in the Likert scale; as a result differences between 'Completely Agree' and 'Agree' and 'Completely Disagree' and 'Disagree' create noise rather than more subtle classification.

Although our current technique is built on simple set-based classification, there is clearly scope to develop statistical models which allow for a more subtle classification procedure.

Our study strongly challenges the simple classification of CC into right-sided versus left-sided disease and underscores the need for a more subtle CC classification, in accordance with recent molecular biological findings.

Our proposed algorithms outperform other classification algorithms, including several subtle multiclass imbalance classification algorithms [ 19, 41, 42 ], in terms of all evaluation criteria for most datasets and especially on the sensitive ones.

These results also demonstrated subtle deficits in classification speed and accuracy in a category search task.

These approaches allow for a multidimensional semantic space where concept features can vary in importance, evolve or change dynamically, accounting for many possible classifications and subtle variations of concept meaning, including the new and the less plausible ones.

Meanwhile, the COG/KOG result was also affected by the subtle difference in functional classification to function similar proteins, especially transporters.

There were no other interactions involving the status of left inferior frontal gyrus, suggesting that both groups and modalities contributed to these effects of lesion location (although this analysis lacks statistical power to detect a subtle interaction with aphasia classification).

Texture features are the intrinsic properties of image and provide an efficient image classification to detect subtle alterations in the gray level distribution of an image [27].

Furthermore, by learning classification models from subtle image features, it may be possible to distinguish between different Parkinsonian syndrome subtypes, such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) from (I123 FP-CIT data.

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