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Computational Information Technology

Computational information technology deals with
problems where efficient numerical methods of computational
engineering are combined with the problems of information
processing. During recent years, computational approach in
information processing has gained increasing importance, with
progress in fields like **computational intelligence**
and **data mining**.
Computational intelligence comprises methods such as neural
networks, probabilistic methods, fuzzy systems and evolutionary
computing, where iterative numerical methods are used for solving
complex problems, involving large amounts of measured data and only
uncertain prior knowledge about the solutions or modeled phenomena.
A central element is inference in novel situations, based on
observed data, that is, inductive learning systems. Similar
capabilities for knowledge discovery are needed in data mining,
where useful pieces of information are sought from large, possibly
unorganized, databases.

In the laboratory the research in computational
information technology is focused in neural computing, Bayesian
methods, and pattern recognition and modeling applications. A good
example of fields in information processing, in which computational
methods have large impact, is the Bayesian approach, where the
estimation of a specific set of model parameters is replaced by
constructing the probability distribution of the model parameters
and integrating over it during the use of the model. This
integration requires efficient numerical methods, such as Markov
chain Monte Carlo methods. The numerical solution also makes it
possible to avoid some standard assumptions (such as linearity
and/or gaussianity), that may be bad in practice. For an overview
of the approach and our results, see review paper
[36].

In the following chapters we give short
descriptions of the research projects in the computational
information technology group.

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