Sentence examples for classification of the task from inspiring English sources

In the literature, we can find approaches[19] where the classification of the task patterns as mandatory or optional is previously defined by a genetic algorithm.

Screenshots with the results from the query construction in ECLECTIC language [ 18] and the query execution results were manually reviewed and a classification of the task completion and success per user were built, indicating three different levels of success (success, failure, and partial success) based on similar studies [ 19].

This is in line with the approach that explains changes in the labour market structure according to a routine/non-routine classification of the tasks performed in jobs.

To ensure that subjects attended to the target classification aspect of the task just as they had done in the experiment proper, we elicited prime-identification responses only on trials in which the target was correctly classified.

The high accuracy in the classification of these tasks (around 70%) allows a quick accomplishment of the experiment designed, even with the low signal-to-noise ratio of this kind of signals.

The four micro-genres found in the writing tasks of the Japanese entrance examinations were compared with results from previous research into the classification of the writing tasks in Japanese entrance examinations and the question of whether those micro-genres are an appropriate set of micro-genres for the Japanese university entrance examinations was investigated.

However, considering all channels provide the best classification performance irrespective of the task or the subject.

Common difficulties in using these two different decompositions include the following: classification of the revealed components (task-related signal versus noise), overall signal-to-noise sensitivity, and the relatively low computational efficiency (multivariate analysis requires the entire raw data set and more time for model identification analysis).

In the phoneme classification case, the formulation of the task is essentially the same as that of static classification; the only difference is that the observations are sequences rather than single values.

For pattern recognition-based classification, the task of classifying a set of images into two classes (such as unipolar v. bipolar depression) can be viewed as a task of finding a decision boundary between two groups.

Depending on the classification method the accuracy of the task of gesture-based person recognition was in the range from 96.60% to 99.29%.