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In the setting of supervised learning on graphs, the target is to train a classifier using a given set of graph data ({mathcal {D}}={(G_i,y_i)}_{i=1}^n), so that we can predict the label (hat{y}) for a test graph G.
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In the setting of semi-supervised learning, one utilizes both labeled and unlabeled data together to improve the performance [ 30].
The setting of the learning task makes it impossible to use supervised or partially supervised techniques to estimate the parameters of the input density.
The main difference between the supervised and semi-supervised K-associated graphs can be stated in relation to the set of neighbors, to which each vertex connects.
In this sense, it includes a wide set of supervised learning algorithms and is able to cope with complex fuzzy systems.
In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines.
In fact, all pairwise distances among labeled images (relevant and non-relevant) are updated according to a single factor λ. Notice that the supervised approach also exploits information provided by the set of non-relevant images, defining a set of negative supervised recommendations (line 11 of the Algorithm ??).
To highlight the differences in gene expression a supervised hierarchical clustering was performed on the set of differentially expressed genes.
The three-month intervention period was administered in the primary care setting and consisted of supervised exercise sessions and diet counselling, followed by regular group meetings during three years.
The HNN MLSE equalizer does not have to be trained by providing a set of training examples as in the case of conventional supervised neural networks [28].
This could be achieved, in part, by increasing access to training programs (particularly in rural areas) and opportunities for practitioners to gain experience in insertions through, for example, practitioners partaking in a day of supervised insertions in setting such as family planning clinics that have higher numbers of patients requesting LARC.
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