Call for Papers
Benelearn is the annual machine learning conference of Belgium and The Netherlands. It serves as a forum for researchers to exchange ideas, present recent work, and foster collaboration in the broad field of Machine Learning and its applications.
Contributions are now being invited that are relevant to machine learning and related disciplines in a broad sense.
We invite the submission of extended abstracts and papers on all aspects of machine learning and related disciplines, including, but not limited to:
- Kernel Methods
- Web/Link Mining
- Bayesian Networks
- Case-based Learning
- Computational Learning Theory
- Data Mining
- Evolutionary Computation
- Hybrid Learning Systems
- Graphical Models
- Inductive Learning
- Inductive Logic Programming
- Knowledge Discovery in Databases
- Language Learning
- Learning and Problem Solving
- Learning by Analogy
- Learning in Multi-Agent Systems
- Learning in Dynamic Domains
- Learning for Bioinformatics
- Multi-strategy Learning
- Neural Networks
- Reinforcement Learning
- Robot Learning
- Scientific Discovery
- Statistical Learning
- Probabilistic Logic Learning
- Computational models of Human Learning
- Learning for Language and Speech
- Applications of Machine Learning
- Learning and Ubiquitous Computing
Submissions are possible as either paper or extended abstract.
Papers should present original, completed and unpublished research. Presentation at the conference will be in the form of a talk.
Extended abstracts may present current, recently published, but also possible future research. For example, you may wish to outline a project that you are interested in, describe and discuss the Big Question in your field or a new or controversial idea or make an appeal for collaboration. Presentation at the conference will be in the form of a speed talk and/or a poster.
Papers should hold a maximum of 6 pages; extended abstracts up to 2 pages.
Papers and extended abstracts should be submitted electronically, no later than Friday, April 2nd, 2010. The only accepted format for submitted papers is PDF.
The reviewing process of the papers will be blind; thus these submissions should not include the authors' names and affiliations or any references to web sites, project names etc. revealing the authors' identity. Each paper submission will be reviewed by at least two members of the program committee. Extended abstracts will also be reviewed, but due to the nature of the extended abstracts reviewing will not be blind. All accepted submissions will be published in the conference proceedings.
Breakout session on teaching machine learning
In addition, we will organize a breakout session on "Teaching Machine Learning". For this session, we invite you to submit an extended abstract describing for example:
- A position statement on teaching machine learning
- Machine learning course contents
- Student project (past, present and/or planned)
- Master thesis projects
Authors of selected abstracts will be invited to present the discussed topic during a special breakout session. Depending on the topic and the overall number of contributions, this might be in the form of a talk, poster or as member of a panel discussion.
Each submission must be submitted online via the Easychair submission interface. Submissions can be updated at will before the submission deadline.
Electronic versions of accepted submissions will be made publicly available on the conference web site.
Papers should hold a maximum of 6 pages and extended abstracts up to 2 pages using the following LaTeX style files.