The European Conference on Machine Learning series is intended 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 are invited that describe empirical and theoretical research in all areas of machine learning. Submissions of papers that describe the application of machine learning methods to real-world problems are encouraged.

Topics of interest (non-exhaustive list):

abduction
analogy
applications
artificial neural networks
Bayesian networks
case-based reasoning
cognitive modeling
computational learning theory
cooperative learning
decision trees
evolutionary computation
grammatical inference
inductive learning
inductive logic programming
information retrieval and learning
instance based learning
kernel methods
knowledge acquisition and learning
knowledge base refinement
knowledge intensive learning
machine learning of natural language
meta learning
multi-agent learning
multi-strategy learning
pattern recognition
planning and learning
reinforcement learning
revision and restructuring
rule induction
robot learning
discovery of scientific laws
statistical approaches
unsupervised learning
vision and learning

There will be a single electronic submission procedure, where authors should indicate whether they submit their paper to ECML, PKDD, or both. In the latter case, the topic of the joint submission must be within the scope of both conferences; accepted joint submissions will be assigned to the more appropriate of the conferences. Student submissions should be clearly indicated on the submission form. All submissions will be reviewed by the respective program committees.

The papers should be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style files can be downloaded   here.  The maximum length of papers is 12 pages.

The proceedings of both conferences will be published as separate volumes by Springer-Verlag in the Lecture Notes in Artificial Intelligence series and will be available at ECML/PKDD.

Simultaneous submissions to other conferences are allowed, provided this fact is clearly indicated on the submission form. Simultaneous submissions that are not clearly specified as such will be rejected. Accepted papers will appear in the ECML/PKDD conference proceedings only if they are withdrawn from proceedings of other conferences.

Data Mining and Knowledge Discovery in Databases (KDD) is a combination of many research areas: databases, statistics, machine learning, automated scientific discovery, artificial intelligence, visualization, decision science, and high performance computing. While each of these areas can contribute in specific ways, KDD focuses on the value that is added by creative combination of the contributing areas. The goal of PKDD is to provide a forum for interaction among all theoreticians and practitioners interested in data mining and KDD.

Topics of interest (non-exhaustive list):

anytime algorithms
applications
collaborative data mining
database integration
dimensionality reduction
discretization
distributed data mining
incremental algorithms
inductive databases
interactive data mining
knowledge discovery process
multimedia mining
OLAP and data warehouse integration
parallel data mining
personalization and adaptivity
preprocessing and postprocessing
prior knowledge integration
relational data mining
scalable algorithms
scientific discovery
text mining
temporal and spatial data mining
visualization
web mining