- Supervised and semi-supervised training – We distinguish between supervised and semi-supervised training.
While availability of supervised data in some Big Data domains can be helpful, the question of defining the criteria for obtaining good data abstractions and representations still remains largely unexplored in Big Data Analytics.
Given the difficulties and limited availability of supervised exercise programmes, an alternative clinically and cost effective approach to increasing the activity levels of patients with CLBP is warranted.
As Virani has stressed before, increased availability of naloxone, supervised drug use spaces, and access to effective addiction treatment are some key actions that need to be taken.
For example, the supervised medication administration and the availability of routine laboratory draws on-site are both added benefits.
For example, less active individuals will benefit more from interventions involving supervised staff combined with equipment availability.
All the anomaly detection techniques can operate in one of the three modes: supervised anomaly detection which assumed the availability of a training data set that labeled instances for normal as well as anomaly class, semi-supervised anomaly detection which only assumed the availability of a normal data set, and unsupervised anomaly detection which did not require training data.
However, as a supervised approach, accurate classification requires the availability of reliable annotated training data for the gene of interest, and cannot be applied for annotating any random sequence extracted from the environment.
The performance of supervised statistical classifiers often depends on the availability of labeled examples.
In a supervised learning, the classification scheme is usually based on the availability of a set of signals that have already been classified or described.
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.
