Call For NECTAR-track submissions
The European Conference on “Machine Learning” and “Principles and Practice of Knowledge Discovery in Databases” (ECML-PKDD) provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning and knowledge discovery in databases and other innovative application domains.
For the first time, ECML-PKDD 2012 will have a NECTAR-track, featuring significant machine learning and data mining results published or disseminated no earlier than 2010 at a different conference or in a journal. One goal of this track is to offer conference attendees the opportunity to learn about machine learning and/or data mining related results published in other communities such as (but not limited to): Artificial intelligence, business informatics, bioinformatics, chemoinformatics, computational intelligence in games, computational linguistics, computer vision, geoinformatics, health informatics, database theory, financial informatics, human computer interaction, information and knowledge management, robotics, pattern recognition, statistics, social network analysis, theoretical computer science.
Papers describing innovative applications of state-of-the-art machine learning and/or data mining algorithms are also welcome, but should be different from demonstration papers; the latter are to be submitted to the demos track. We also invite submissions presenting compactly well-founded results which appeared in a series of publications that advanced a single novel influential idea or vision.
Submissions are limited to four pages and should put the problem and the solution into the context of general machine learning and/or data mining. Although papers describe previously published results, submissions must be original. The summary of the actual, previously published, result(s) should take no more than two pages.
Submission guidelines and review process
Submissions will be reviewed by two or more independent referees. Selection criteria include the relevance of the contribution, its interests and usefulness for attendees, its technical strength and originality for the machine learning and data mining community.
Accepted NECTAR contributions will be presented as talks or posters and included in the conference proceedings. The papers must be written in English and formatted according to the Springer LNAI guidelines. Authors instructions and style files can be downloaded at: http://www.springer.de/comp/lncs/authors.html
With the submission, authors are required to send an electronic copy of the original publication(s) as well as a few sentences motivating why the paper is appropriate for the NECTAR track of ECML-PKDD 2012. Furthermore, the original publications must be cited clearly in the introduction of the submission or as a footnote to the title.
All aspects of the submission and notification process will be handled online via the CMT conference management toolkit at https://cmt.research.microsoft.com/ECMLPKDD2012/.
- submission of NECTAR contributions: May, 18th
- notification of acceptance: June, 15th
- submission of camera ready copy: June, 29th
For enquiries please contact the Nectar Track Chairs Thomas Gaertner and Gemma Garriga at
- Aristides Gionis – Yahoo! Research Barcelona
- Burr Settles – CMU
- Christina Leslie – Memorial Sloan-Kettering Cancer Center
- David Hardoon – SAS Singapore
- David Page – University of Wisconsin Medical School
- Gyorgy Turan – University of Illinois at Chicago
- Ivan Titov – Saarland University
- Jean-Philippe Vert – Mines ParisTech & Curie Institute
- Liva Ralaivola – University Aix-Marseille, France
- Marc Deisenroth – TU Darmstadt
- Pádraig Cunningham – University College Dublin
- Roni Khardon – Tufts University
- Steffen Rendle – University of Konstanz
- Xavier Carreras – Universitat Politecnica de Catalunya, Spain
- Jason Badridge – University of Texas at Austin
- Kristian Kersting – Fraunhofer IAIS, University of Bonn
- Zhao Xu – Fraunhofer IAIS
- Mohammad Ghavamzadeh – INRIA Lille
- Kurt Driessens – Maastricht University