Solving Complex Machine Learning Problems with Ensemble Methods


ECML/PKDD 2013, September 27, Prague, Czech Republic

Submission guidelines

Two types of submissions are invited: paper submissions and problem submissions.

Paper submissions must be written in English and formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style files can be downloaded from here .

Two types of paper submissions are allowed: short papers and research papers. The maximum length of short papers is 6 pages in the format described before. The maximum length of research papers is 16 pages, although papers of up to 12 pages are preferred. Submitted papers will be peer-reviewed by at least three reviewers. Acceptance will be based on the basis of these reviews and on relevance, technical soundness, originality, and clarity of presentation. Accepted papers will be presented at the workshop either as a poster or via oral presentation.

The EasyChair electronic submission system will be used for handling workshop. Click here in order to login at EasyChair and submit the paper.

Extended versions of selected papers will be considered for a Special Issue in Neurocomputing journal (Elsevier, http://www.journals.elsevier.com/neurocomputing/).

Problem submissions have the objective to start interactions from the community. In particular, researchers are invited to submit:

Problem submissions will be uploaded in the workshop web site as either:

The problems will be briefly presented in the Networking Session by the authors or the workshop chairs (in case authors are not present). The goal will be then to obtain feedback from the community of experts in ensemble methods attending the workshop. The schema of this will be as follows:

The problems will be visible to the community and the contact information of the authors will be available on the site. A message board will be enabled under each problem to facilitate discussion between the different members of the machine learning community.