(apologies for multiple copies)
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Call For Demos
ECML/PKDD 2008
The European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases
September 15-19, 2008
Antwerp, Belgium
http://www.ecmlpkdd2008.org/
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At ECML PKDD 2008 a special demonstration session will be held, which
is intended as a forum for showcasing the state of the art in machine
learning and knowledge discovery software. The focus will be on
innovative prototype implementations, systems and technologies in
machine learning and data analysis. We strongly recommend that you
make
your demonstration open source, but do not require this for
submission.
Submissions will be judged by a committee of technical experts with
experience in developing software for machine learning and data
analysis. They will select demonstrations based on their technical
merits and their interest and usefulness for attendees of the
conference.
Accepted demonstration papers will be published in the conference
proceedings (4 pages). At least one of the authors must register for
and attend the conference in order to present the demonstration.
Technical details about the presentation will be sent with the
acceptance notification.
Key Dates
(identical to normal paper submission key dates)
* Demo Submission deadline: April 25
* Demo Acceptance Notification: June 13
* Demo Camera Ready: June 22
Submissions
Each demonstration should be accompanied by a short paper of at most
4 pages (including figures and screenshots if needed). The paper has
to be submitted through the normal conference paper submission system.
The paper must be in English and must be formatted according to the
Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.
Authors instructions and style files can be downloaded at
http://www.springer.de/comp/lncs/authors.html.
In this accompanying paper, please try to answer the following
questions: What makes your piece of software unique and special?
What are the innovative aspects or in what way/area does it represent
the state of the art? For whom is it most interesting/useful? (an ML
or KDD researcher, a graduate or undergraduate student in these areas,
an industrial practioner etc.) If there are similar/related pieces of
software: What are the advantages and disadvantages compared to these
related software?
Contact
In case you have any question, please do not hesitate to contact
the ECML/PKDD 2008 Demo Chair:
Christian Borgelt, christian@[EMAIL PROTECTED]
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