Recent Advances in Pattern Classification - Computational Intelligence Paradigms in Advanced Pattern Classification - page 2

The requirements of an advanced user are not limited to just collecting, proc-
essing and analysing information in computer systems. Today, users expect IT
systems to offer capabilities of automatically penetrating the semantic layer as
well, as this is the source for developing knowledge, and not just only collecting
messages. This is particularly true of information systems or decision support sys-
tems. Consequently, IT systems based on cognition will certainly be developed in-
tensively, as they meet the growing demands of the Information Society, in which
the ability to reach the contents of information collected in computer systems will
be gaining increasing importance. In particular, due to the development of system
which, apart from numerical data and text, also collect multimedia information,
and particularly images or movies, there is a growing need to develop scientific
cornerstones for designing IT systems allowing one to easily find the requisite
multimedia information, which conveys a specific meaning in its structure, but
which requires the semantic contents on an image to be understood , and not just
the objects visible in it to be analysed and possibly classified according to their
form. Such systems, capable of not only analysing but also interpreting the mean-
ing of the data they process (scenes, real-life contexts, movies etc.), can also play
the role of advisory systems supporting human decision-making, whereas the ef-
fectiveness of this support can be significantly enhanced by the system automati-
cally acquiring knowledge adequate for the problem in question.
Fig. 1 Taxonomy of issues expplored by cognitive science
It is thus obvious that contemporary solutions should aim at the development of
new classes of information systems which can be assigned the new name of Cog-
nitive Information Systems. We are talking about systems which can process data
at a very high level of abstraction and make semantic evaluations of such data.
Such systems should also have autonomous learning capabilities, which will allow
them to improve along with the extension of the knowledge available to them, pre-
sented in the form of various patterns and data. Such systems are significantly
more complex in terms of the functions they perform than solutions currently em-
ployed in practice, so they have to be designed with the use of advanced