In this work, we propose to extend the generative Gaussian graph to supervised learning in order to extract the topology of labeled data sets.
For this purpose, SVMs are adopted for supervised classification in order to identify the most favorable design configurations associated with suppressed SHEs and improved electrical performance.
Differently, the derived term groups are then encoded as new features for the supervised learners in order to improve the coverage of lexical information.
The learning curve for EBUS-TBNA has been evaluated and recorded at ten supervised procedures in order to achieve excellent sensitivity and diagnostic accuracy [14].
Assuming that Indian zebu and Bali cattle are the ancestral populations we then carried out supervised clustering in order to estimate the levels of introgression via the individual membership coefficients (figure 2, table 1).
For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs.
Therefore, it is necessary to consider a semi-supervised method in order to induce a classifier from both labeled and unlabeled patterns.
For these reasons, we focus on fuzzy supervised classification methods in order to discriminate these classes.
In this field we have chosen to use fuzzy supervised classification methods in order to design a diagnosis system or a process-monitoring module.
The Sum-of-Squares error is optimal for training supervised learning machines in order to detect or to classify Gaussian signals.
For each layer, we have to construct its own artificial classification problem and execute the supervised learning process in order to determine the similarity of examples.
