Sentence examples for data are labelled from inspiring English sources

In supervised classification, the training data are labelled based on the class, such as defect type or location.

Outcomes where the system agrees with the actual data are labelled true positives (TP) or true negatives (TN) depending on whether the pixel belongs to an object or not.

Col/Col homozygous data are labelled and highlighted with an arrow in each plot.

We may assume that the data are labelled in a non-descending order, i.e. that x i ≤ x i + 1.

Illustrative quotations from data are labelled with the age and gender of the patient, time since hip fracture and whether the quotation was from the patient or carer.

Traditionally, machine learning has been studied either in the unsupervised paradigm (e.g., clustering) where all the data are unlabelled, or in the supervised paradigm (e.g., classification) where all the data are labelled.

Being different from EMO-DB, VAM data are labeled by 17 human labelers on a 5-point scale for three dimensions (valence, activation, and dominance).

The error bars of the experimental data are labeled.

Since training data are labeled under two classes (positive and negative) for the sentence-level categorization, ROC (Receiver Operating Characteristic) curves are also plotted for a better performance comparison.

For knockdown data, the Z-SCORE method loses its top rank, and semi-supervised methods perform better when at least 70% of the data are labeled.

A persistent problem is that some classes of molecular data are labeled with gene identifiers, others with transcript or protein identifiers, and still others with chromosomal locations.