[DL] ECML-PKDD 2016 Upcoming Deadlines: Demos and Nectar tracks

Olana Missura missura.olana at gmail.com
Tue May 3 14:43:36 CEST 2016

ECML-PKDD 2016 Upcoming Submission DeadlinesDemos Track


   Demo submission deadline: Friday, May 13, 2016

   Notification of acceptance: Monday, June 20, 2016

   Camera-ready paper due: Friday, July 1, 2016

Submissions must describe working systems and be based on state-of-the-art
machine learning and data mining technology. These systems may be
innovative prototype implementations or mature systems that use machine
learning techniques and knowledge discovery processes in a real setting.
Systems that use basic statistics are not acceptable. A commercial software
is not acceptable.

The accepted papers for demos will be included in the conference
proceedings, to be published by Springer Verlag in the "Lecture Notes in
Artificial Intelligence" (LNAI) Series. The demos will be presented in a
special demonstration session. At least one of the demo submitters must
register for the conference, and perform the demo on site.

Additional information about the submission guidelines is available at
Nectar Track


   Submission deadline: Friday, May 13, 2016

   Notifications of acceptance: Monday, June 20, 2016

   Submission of camera ready copies: Friday, July 1, 2016

The goal of the Nectar Track, started in 2012, is to offer conference
attendees a compact overview of recent scientific advances at the frontier
of machine learning and data mining with other disciplines, as published in
related conferences and journals. We invite senior and junior researchers
to submit summaries of their own work published in neighbouring fields,
such as (but not limited to) artificial intelligence, data analytics,
bioinformatics, games, computational linguistics, natural language
processing, computer vision, geoinformatics, health informatics, database
theory, human computer interaction, information and knowledge management,
robotics, pattern recognition, statistics, social network analysis,
theoretical computer science, uncertainty in AI, network science, complex
systems science, and computationally oriented sociology, economy and
biology, as well as critical data science/studies.

Particularly welcome is work that summarises a line of work that comprises
older and more recent papers. The described work should be relevant to a
broad audience within ECMLPKDD, and (a) illustrate the pervasiveness of
data-driven exploration and modelling in science, technology, and the
public, as well as innovative applications, and/or (b) focus on theoretical

Additional information about the submission guidelines is available at
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