Ten children, aged between 6.1 and 9.6 years, were taught the relevant vocabulary to label a set of emotions (e.g., happy, sad, angry), to match these tacts to illustrated situations, to generalize these tacts to novel situations, and to tact their own emotions.
Privacy officials then label a set of suspicious and non-suspicious access events using an iterative refinement process.
We propose using the Focus to Emphasize Tone (FET) analysis, which includes: (i) generating the constraints for foci, speaker's intention and prosodic features, (ii) defining the intonation patterns, (iii) labelling a set of prosodic marks for a sentence.
In order to evaluate the proposed algorithm a large ground truth dataset was created by manually labelling a set of 100 images from 20 different colonoscopy videos.
This labeling is called distant supervision because the user automatically labels a set of training data using known entities or relationships from an independent data source.
Moreover, specialists labeled a set of cells belonging to images with fluorescent cells since our recognition approach requires the labels of individual cells to train the corresponding classifier.
Thus, anchored training provides a way to build supervised models without the high cost associated with manually labeling a set of training instances.
To train a classifier using a C-SVM and a linear kernel we labeled a set of detected indel candidates by reliable Sanger sequencing as true (positive class) and false indels (negative class).
After inferring labels, a set of annotated examples is generated by associating high dimensional temporal data to one dimensional target labels inferred from time series of interest, begin{aligned} forall x_i in X, x_i rightarrow l_i, D = left{ left( x_{1},l_{1} right), left( x_{2},l_{2} right),ldots,left( x_{N-Delta r},;l_{N-Delta r} right) right}.
Other terms that appear to label an overlapping set of phenomena are Global Public Private Partnerships [ 4, 5] and Global Health Partnerships [ 6].
For a feature label or a set of feature labels, the fusion planning engine is to discover and schedule a sequence of algorithms in the algorithm pool triggered by the feature label(s).
