Within the framework of the joint project of Linnaeus and Gothenburg
universities, Sigma Technology and Ericsson companies, we are looking for students
to work on Master`s thesis. The thesis topic is “Machine learning for assessing
information quality (30 credits)”. The ideal candidate is expected to be
familiar with machine learning; scientific precision in working and documenting
the results is a must.
Technical
documentation, such as user manuals and technical specifications, often
constitutes the first line of support when users need help or want to learn
more about a product or a service. It becomes an important part of user
experience and the information provided needs to be of appropriate quality.
Machine learning gets more and more important in all areas of software
technologies. Applications range from system optimization and testing to
adaptive software architectures. This project develops a machine learning
approach to information quality assessment.
In our work we
use machine learning to define what users consider high information quality.
Therefore, a number of experts will judge quality of sample documents. At the
same time, we automatically assess some attributes of these documents. This
training data is input to classification (or linear regression) of algorithms.
As a result, we get a classifier (or scorer) able to judge quality of yet
unknown documents based on automatically assessed attributes.
The project is
evaluating a novel hypothesis. It has both high scientific potential and
practical relevance. It gives the insight knowledge in machine learning and
artificial intelligence, one of the most exciting technologies that computer
science has ever developed. Expected timeframes are Spring
– Autumn 2014.
Main requirements:
·
familiarity with
machine learning
·
scientific precision
in working and documenting the results is a must
·
familiarity with software
metrics would be a plus
Your
applications please send to:
Welf Löwe, Welf.Lowe@lnu.se