The paper submission deadline has passed. The abstract submission deadline was April 26th. The full paper had to be submitted until May 3rd.

Call for Papers

The 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) will be co-located in Berlin, Germany, September 18th-22nd, 2006. The combined event will comprise presentations of contributed papers and invited speakers, a wide program of workshops and tutorials, and a discovery challenge.

Important dates

Abstract submission deadline April 26th, 2006
Paper submission deadline May 3rd, 2006
Acceptance notification June 16th, 2006
Camera-ready copies due June 30th, 2006

Workshops and Tutorials will be held on September 18th and 22nd, please see the call for proposals. The main conference will most likely start in the afternoon of September 18th and end on September 22nd, noon.

Paper Submission

High quality research contributions pertinent to any aspects of machine learning and knowledge discovery are called for, ranging from principles to practice; particular attention will be paid to papers describing innovative, challenging applications.

There will be a single electronic submission procedure, where authors must indicate whether they submit their paper to ECML or PKDD. There will be a joint programme committee consisting of area chairs and reviewers for both conferences. In order to allow a meaningful assignment of your paper to the most suitable area chair and reviewers, it is important that you indicate the content of the paper with a suitable set of keywords from the provided list. There will be no "blind review" process. Student submissions should be clearly indicated on the submission form.

The papers must be in English and must be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style files can be downloaded at The maximum length of papers is at most 12 pages in this format.

Double submissions to the KDD conference are allowed.

ECML Call for Papers

The European Conference on Machine Learning series intends to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scientific event in the field. Submissions of papers that describe the application of machine learning methods to real-world problems are encouraged, particularly exploratory research that describes novel learning tasks and applications requiring non-standard techniques. In particular, we are interested in theoretical and empirical contributions to the following areas:

  • artificial neural networks
  • bayesian networks
  • case-based reasoning
  • clustering
  • computational models of human learning
  • computational learning theory
  • constructive induction and theory revision
  • cooperative learning
  • decision tree learning
  • discovery
  • ensemble methods
  • evaluation of learning methods
  • incremental induction and on-line learning
  • inductive logic programming
  • information retrieval and learning
  • instance based learning
  • kernel methods
  • knowledge base refinement
  • knowledge intensive learning
  • machine learning of natural language
  • meta learning
  • multi-agent learning
  • multi-strategy learning
  • planning and learning
  • prediction of complex structures
  • regression
  • reinforcement learning
  • rule learning
  • statistical approaches
  • semi-supervised learning
  • unsupervised learning
  • vision and learning

PKDD Call for Papers

The European Conference on Principles and Practice of Knowledge Discovery in Databases celebrates its 10th year as an international forum for the state of the art in the interdisciplinary field of knowledge discovery and as the major European scientific event in this domain. We invite submissions that report original results on leading-edge subjects of knowledge discovery from conventional and complex data, adhering but not limited to the following topics of interest:

  • Adaptive and incremental algorithms
  • Applications of data mining
  • Foundations of data mining
  • Knowledge modeling and exploitation in data mining
  • KDD frameworks and process models
  • Innovative algorithms
  • Mining and induction in databases
  • Mining data streams
  • Mining links, graphs, trees, sequences and high-dimensional structures
  • Mining for information retrieval
  • Mining text, hypertext and semi-structured data
  • Multimedia data mining
  • Multirelational data mining
  • Pre-processing methods for data mining
  • Post-processing and maintenance of data mining patterns
  • Spatial and temporal data mining
  • Visualisation of data mining patterns
Photo by Land Berlin/Thie