  Semi-supervised learning (SSL) is a class of supervised learning tasks and
techniques that also exploits the unlabeled data for training. SSL
significantly reduces labeling related costs and is able to handle large data
sets. The primary objective is the extraction of robust inference rules.
Decision support systems (DSSs) who utilize SSL have significant advantages.
Only a small amount of labelled data is required for the initialization. Then,
new (unlabeled) data can be utilized and improve system's performance. Thus,
the DSS is continuously adopted to new conditions, with minimum effort.
Techniques which are cost effective and easily adopted to dynamic systems, can
be beneficial for many practical applications. Such applications fields are:
(a) industrial assembly lines monitoring, (b) sea border surveillance, (c)
elders' falls detection, (d) transportation tunnels inspection, (e) concrete
foundation piles defect recognition, (f) commercial sector companies financial
assessment and (g) image advanced filtering for cultural heritage applications.
