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Competency-based training can be achieved using simulation, web-based learning tools, and formal hands-on training to allow a clinical procedure to be learned based on a system of standard metrics so that progress and proficiency can be recorded, followed, and assessed [8-11].

For this "oddball" model, words would be learned based on the lack of associative connections to the already known words as opposed to being primed for learning by associative connections; a sign change in the sensitivity parameter of the lure of the associates model would create this difference.

In our case, a rule can be learned based on provided training data, and it can be applied to the whole genome to predict new targets of a specific TF.

Blum and Mitchell (1998) proved that for a classification problem with two such feature sets, the target concept can be learned based on a few labeled and many unlabeled examples, provided that the views are compatible and uncorrelated.

Rather than relying on basic extrinsic reward signals from primary reinforcers, it has been suggested that hierarchies of skills could be learned based on intrinsic reward signals that reinforce unexpected salient events (Chentanez et al. 2004).

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Training data is not needed in the unsupervised approach, where sentiment categories are learned based on text-based corpus.

Following a BoW pipeline, a linear SVM is learned based on the BoW representations.

Finally, a target classifier is learned based on the reweighted source instances and any labeled target instances that are available.

The missing disciplines (not previously extracted from Wikipedia) have been learned based on the thesis title using a Bayesian supervised dictionary learning technique.

The CRF model is learned based on sparse features of local patches in a multi-scale structure and it also takes the contextual information of target into consideration.

In training phase, a feature basis matrix is learned based on NMF by considering color information and a feature detector is designed by performing Schmidt orthogonalization on the feature basis matrix.

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