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, as well as their application in innovative application domains.
The 2013 edition of ECML PKDD has, next to the usual proceedings track, a new journal track in collaboration with Machine Learning and Data Mining and Knowledge Discovery. Articles can be submitted to the journal track on a continuous basis until early March. The proceedings track has one deadline in April.
Submissions to the journal track should meet the standards of the journals, and, additionally, be concise and lend themselves to oral presentation at a conference. Articles focusing on consolidation of earlier work are less suitable for this track. Submissions will benefit from a streamlined reviewing process that allows for notification within 8 weeks. Resubmission of revised versions is possible. Upon acceptance, submissions automatically earn a presentation slot at the conference, and an abstract of the article will be included in the proceedings. More information about this new submission model is available here.
Submissions to the proceedings track should meet the traditional standards of ECML PKDD in terms of novelty, significance, and readability. These submissions may describe work that is in a less finished state than for journal articles. They should be concise, counting at most 16 pages (and preferably less), in LNCS format.
Submissions are invited on all aspects of machine learning, knowledge discovery and data mining, including real-world applications. Journal submissions should present work that is novel, timely, and constitutes a clearly delineated piece of research that can be considered finished. Submissions to the proceedings track ideally present innovative ideas that are inspiring, provoke discussion, and/or are demonstrated to have a large potential.
Important criteria for all submissions are their:
- potential to inspire the research community by introducing new and relevant problems, concepts, solution strategies, and ideas
- 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
- scientific rigor, correctness, reproducibility of experiments
- presentation quality: preciseness and clarity is required
See KEY DATES.