Sentence examples for supervised steps from inspiring English sources

To date there seems to be no consensus on whether it is necessary to include all steps in CV: Some authors postulate that all steps are required to be included [ 9], which seems to be done rarely, regardless; others only suggest this procedure for variable selection [ 3] or more general supervised steps [ 10].

In the second step the positions of objects are projected to the output layer, where the weights are adjusted to output values (supervised step).

Using the revised supervised step described in this work without incorporating prior probabilities into the iBMA framework, iBMA-shortlist yielded a TPR of 12.78% and O/E ratio of 2.92.

The discrepancy between e incompl, K (s ) and e full, K (s ) depends on how strongly the specific preliminary step conducted on the whole dataset increases the homogeneity of the covariate values across observations and (for supervised preparation steps) the empirical association between the covariate values and the values of the target variable.

The supervised training step then leads these distinguishing characteristics in the right direction, hence the need for a pre-training step.

And then you let the supervised learning step somewhat project that unreasonably sized model.

We describe a sketch auto-completion framework that addresses these challenges by learning visual appearances of partial drawings through semi-supervised clustering, followed by a supervised classification step that determines object classes.

As expected, most of the centrality metrics are very correlated indicating that some of them can be discarded in the supervised learning step.

The final supervised classification step of an image analysis pipeline is often a bottleneck robustness and speed in the overarching framework are the key to solving this, according to Chen et al. As any practitioner of machine learning knows, there are some situations in which we do not have labels for all of our training data.

SAE is a hybrid learning method, which includes both supervised and unsupervised steps.

The proposed algorithm exploits both labeled and unlabeled data aiming at improving the effectiveness of image retrieval tasks considering supervised and unsupervised steps.