Sentence examples for labelling the training from inspiring English sources

In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment.

The common ML algorithms used un NER are: Supervised learning algorithms learn and offer feedback on the learning process (supervised learning) by labelling the training instances with the correct results.

Manually generating and labeling the training data is naturally an expensive solution in terms of time and effort.

The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set.

They tried to increase the accuracy of classifiers by labeling the training set manually and then recognized the emotion tendency of the original text.

Learning from label proportion makes it possible to predict the label of each sample by labeling the training samples group by group, which shows potential for efficient classification of HR SAR images.

So, researchers need to label the training samples more precisely and more carefully and even need the support of professional interpreters, field surveys, or other relevant information, such as ancillary multispectral, geographic information system (GIS), or elevation data [3, 4].

In such situations, when the codes are used to label the training data, we should proceed with caution as they cannot entirely be considered as a gold standard.

Once the abnormal and normal clusters are labeled, the trained clusters along with their labels are passed to the classification stage.

When the data has no labeling, the training is unsupervised.

The main motivation behind this proposed technique is to improve the classification performance with the significantly reduced requirement of the labeled training data and training time.