Conference Information
ECML-PKDD 2015 : The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
http://www.ecmlpkdd2015.org/
Submission Date:
2015-03-26
Notification Date:
2015-06-01
Conference Date:
2015-09-07
Location:
Porto, Portugal
Years:
30
CCF: b   CORE: a   QUALIS: a2   Viewed: 4999   Tracked: 8   Attend: 0

Conference Location
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Call For Papers
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 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.

We invite submission of high quality, original papers describing innovative research on all aspects of machine learning, knowledge discovery and data mining, including real world applications. Papers emphasizing theoretical foundations as well as novel modelling and algorithmic approaches to specific machine learning and data mining problems are encouraged. All application areas are relevant, including scientific, medical, business, government, sustainability and engineering. Given the rising importance of big data, contributions on high performance and distributed platforms, systems and algorithms are welcome.

Papers will mainly be evaluated on the basis of their relevance for the conference, their scientific contribution, rigour and correctness, the quality of presentation and reproducibility of experiments. We note that papers may be rejected for lack of clarity. Additionally, important criteria for submissions are:

    Potential to inspire the research community by introducing new and relevant problems, concepts, solution strategies and ideas, even if the work is at an early stage of development;
    Contribution to solving a problem widely recognized as both challenging and important;
    Capability to address a novel area of impact of machine learning and data mining.

Proceedings
The conference proceedings will be published by Springer Verlag in the Lecture Notes in Artificial Intelligence Series (LNAI). 

Apart from making normal conference submissions, there is also the possibility of submitting papers to the ECML PKDD 2015 journal track.

Submissions
All aspects of the submission and notification process will be handled online via the conference CMT submission site.

To submit a paper:

    Create an account and Log in to CMT. Please note that user accounts in each CMT conference is independent of other conferences. Thus, the credentials for any previous CMT conference will not work for ECMLPKDD'15.
    Specify your conflict domains.
    Create a New Paper submission.
    Select the  Scientific Track.
    Complete the submission form by providing title, authors, one primary subject area, any number of secondary subject areas and a short abstract. The manuscript must be uploaded in pdf format.  You will also need to answer the questions in site.

The papers must be written in English and formatted according to the Springer LNAI guidelines. Author instructions, style files and copyright form can be downloaded at Information for Authors of Computer Science Publications​.

The papers should be concise, counting at most 16 pages (preferably less), in LNCS format. Both overlength papers and papers that are not in the LNCS format will be rejected without review.

Papers submitted should report original work. ECML PKDD 2015 will not accept any paper that, at the time of submission, is under review or has already been accepted for publication in a journal or another conference. Authors are also expected not to submit their papers elsewhere during the review period.

Submissions where the first author is a fulltime student should be clearly indicated on the submission form.

Reviewing Process
Papers submitted to ECML PKDD 2015 will normally be reviewed by three referees. The review process will be single blind. Submitted papers will be evaluated on the basis of significance of contribution, novelty, technical quality, scientific and technological impact, clarity, repeatability and scholarship. Authors are strongly encouraged to make data and code publicly available whenever possible. Accepted papers will have a summary of the paper's reviews available online, if authorized by the authors.
Last updated by Dou Sun in 2015-03-07
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