Privacy officials then label a set of suspicious and non-suspicious access events using an iterative refinement process.
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.
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).
In KP-ABE scheme [13], the data access policy (denoted as Au_KP) is specified by data users; the ciphertext is labeled by a set of attributes (denoted as A_o).
Positive examples were hand labeled on a set of 81 images from a single training video while 10 negative examples for each feature were randomly picked from the same images.
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}.
