Clustering algorithms can be categorized into unsupervised and semi-supervised approaches depending on whether we have certain prior knowledge about the clusters.
The requirement to undertake supervised practice/adaptation varied significantly depending on country of training.
We explain how to train them using a supervised or an unsupervised learning depending on the task we want to do: regression, discrimination or clustering.
Compared with those, our modeling approach can range between supervised and unsupervised signal identification, depending on the amount of additional information incorporated into the model.
Within data mining techniques it is possible to differentiate two main sets of algorithms, depending on the use (supervised learning) or not (non-supervised learning) of previous knowledge on the data.
Depending on the interface, supervised or unsupervised analysis (see below) can be pre-calculated or computed on demand.
Finally, depending on the algorithm or supervised learning method, results may be more or less sensitive to experimental changes than those reported with SSEM-Lasso.
PathVar identifies and analyzes deregulation patterns in pathway expression using two possible analysis modes, a supervised and an unsupervised mode, chosen automatically depending on the availability of sample class labels.
Depending on the problem under study, supervised or unsupervised approaches may be necessary.
Therefore, the same individual appeared to use different criteria depending on whether they marked their supervised student's report or others.
