Sentence examples similar to training and labelling from inspiring English sources

A production schedule, training and labeling procedures should also be considered.

Among them, Ritter et al. [44] proposed an NER system for tweets, called T-NER, which employed a CRF model for training and Labeled-LDA.

Higher orders of the CRF in combination with high order offset conjunctions lead to the best results observed (F1 measure 85.6%) on the MEDLINE test corpus with an CRF order 3 and an offset conjunction of 2. On this corpus also the direct dependency of training and labeling durations to the orders of CRF and offset conjunction are shown (cf. Fig. 7).

Swenson et al. suggested that the pVL measurement at week eight was a good indicator of treatment success, which is why we used it to compute training and test labels.

When all training instances and labels are considered all together in a single optimization problem, multi-label support and core vector machines (i.e., Rank-SVM and Rank-CVM) are formulated as quadratic programming (QP) problems with equality and bounded constraints, whose training procedures have a sub-linear convergence rate.

Our work is in a similar spirit where we learn our dictionary from training data and label any test spectrum that does not correspond to our dictionary as being anomalous.c The authors would like to thank Prof. Roummel Marcia for fruitful discussions related to this point.

When parents strive for connection, self-awareness and a deeper understanding of their child, they'll be on the way to attaining the title of parenting 'speedcuber' (yes that is actually a term people train for and label themselves as, in the Rubix cube circuit), and the benefits to both parent and child, as a result, will be immeasurable.

There have been studies to mitigate the effect of a small training set [27, 28] by iteratively labeling unlabeled data with the classifier under training and adding newly labeled samples to the training set.

This is perhaps the simplest classification algorithm, and involves, for each test point, finding the k-closest training points to it and labelling the test point by a majority vote.

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

The data set information and the sizes of whole training data set and labeled data set are detailed in Table 1.